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Monday’s essay ended with Pope Leo XIV refusing the comfortable theological floor of dignity and walking toward the labor floors. He gestured at contribution and consent, but left procedural alone. The encyclical was theological in form and labor doctrine in content.

Three floors still need walking. The contribution floor needs the chain the Pope only pointed at. The consent floor needs the institutional-versus-relational pin that the encyclical implies but does not hold. The procedural floor never got a Vatican stage Monday at all. This is the field guide entry that walks them.

The “floors” come from Richard Feynman. Asked in a famous interview why magnets attract, he refused the surface question and asked back: what do you mean by “why”? Every why-question rests on a floor, an assumption the listener has agreed to stop questioning. Like a market, justice, God, history, and lived experience floor. The floor depends on the listener, and the same word means different things depending on which floor it lands on. Monday’s essay used Feynman’s coceptual framing to set up the four floors of dignity. Wednesday walks the three the Pope did not take.

I had planned to write this alone. However, the week handed me three writers instead. Arimitsu published a piece called “Layers of Sameness” that maps the five different things people can mean when they say two words refer to the same thing. Chris Stephens reshared an April 30 essay this morning on LinkedIn that extends my earlier “accountability” piece into structural territory I had not taken it. Eric Patterson sent me a Hollis Robbins essay on what “higher” means in higher education. The Robbins piece is for my Friday Bindings. The other two are working below. As you can tell, I love to collaborate on knowledge…because that’s What Time Binds: knowledge.

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The diagnostic Arimitsu handed me

Arimitsu’s argument, compressed: when two people say the same word in a meeting, they may be measuring sameness by five different criteria, each one operating like a different floor of the same building.

Category-same. Two Tokarev pistols. Different factories, different metallurgy, different machinists, same blueprint. The word stays steady because it points at the design.

Descent-same. Theravāda Buddhism and Zen. Both descend from the same source. The lineage holds the name. What gets done in any given room varies widely.

Collectively-agreed-same. The Kusanagi sword at Atsuta Shrine. Almost no one has seen it. It counts as real because a community agreed to treat it as real.

Recognition-anchored-same. The ship of Theseus. The name was attached at a moment of public recognition. The boards have all been replaced. Whether the object still has any right to the name depends on how far you have drifted from the moment of attachment.

Relationship-same. The merchant knows the ship is a forgery. The boy who saved up to buy it treasures it as authentic. Same object. Two different verdicts. Both internally coherent. Both measuring by criteria the other isn’t using.

A magnet word like dignity sits on top of all five layers. The four floors I described Monday are typologies of where the word lands. The five layers are typologies of which kind of sameness the speaker is claiming when they use it. Floors plus layers is the real coordinate. A pin in the room needs both. The complexity stays, and the comprehension improves.

The procedural floor

Procedural dignity is the floor where people stand when they say “we will treat you fairly because we have a fair process.” The pin is the process. The dignity is in following the procedure, applying it consistently, giving everyone access to the same set of steps.

The default layer here is collectively-agreed-same. We all said this is the procedure. Recognition-anchored-same operates underneath: the procedure was set up at a moment when the community said, “this is the way.” Both work as long as nobody asks how the procedure got built and who was there when it did.

Chris Stephens named the specific failure mode this floor produces. She reshared the diagnosis on LinkedIn this morning. The pattern is “accountability without authority.” Organizations routinely hold people accountable for outcomes they never controlled. The procedure assigns responsibility, which is procedural dignity, doing something else. It also withholds the authority required to discharge that responsibility. The procedure says you matter. The structure says you do not get to act on what matters.

Same word, opposite function. An organization built around repair and harm reduction uses “accountability” to maintain coherence. An organization built around profitability uses the same word to manage optics and locate blame. The procedural floor honors a word both kinds of organizations use. Most people cannot tell which one they are using until the consequences arrive.

The failure mode in Arimitsu’s terms: descent-same gets erased. The procedure looks the same. Where it came from, what it was originally for, and who it was designed to protect have all dropped out. The category-same of “we have a process” runs over the descent-same of “this process came from this history, with these original includes and excludes.” Procedure without lineage is procedure that cannot tell whether it is doing the work it was built to do.

The contribution floor

Contribution dignity is the floor where people stand when they say “you have earned a place in this.” The pin is the contribution. The dignity tracks what you have built, made, fixed, raised, taught, served, or fought for.

The default layer is category-same. You are in the category of people who contribute. Relationship-same operates underneath in particular cases: your specific work in this team, this family, this unit. Both layers work when contribution is visible and recognized.

The Pope pointed at this floor Monday. He did not walk it. He named the AI labor supply chain (content moderators, miners, data labelers) and said their contribution is what makes the visible AI possible. The gesture was clear. The pin still needs writing.

The failure mode this floor produces, in Arimitsu’s terms: descent-same gets erased. The category-same of “we recognize contributors” runs over the descent-same of “the contributions that built the floor everyone is standing on.” The recognized contributor is the visible one, in the current cycle, doing the legible work. The contributions that came before, the contributions that made the visible work possible, the contributions that got recoded as “support” or “service” or “inputs” all drop out.

The clearest current example is in higher education, which is what Hollis Robbins walks in a piece Eric Patterson sent me this week. The work that builds the visible contributor depended on contributions that the recognition system never coded as contribution at all. Same shape of failure, different room. Robbins gets the Friday Binding treatment.

The consent floor

Consent dignity is the floor where people stand when they say “you cannot be acted on without your agreement.” The pin is consent. The dignity tracks whether the people in the action gave the action permission.

The default layer is relationship-same. Consent happens between specific parties. The merchant and the buyer. The doctor and the patient. The participant and the researcher. Descent-same operates underneath: the modern human rights tradition is the lineage that built the current understanding of what consent requires.

The Pope gestured at this floor Monday in the language of new forms of slavery. Workers in the AI supply chain cannot refuse the conditions of their labor. That is one shape of consent failure. The AI training data question is another. The people whose words, faces, voices, code, and art trained the current generation of models were not asked. The models were trained on their work as if the work were available.

The failure mode in Arimitsu’s terms: collectively-agreed-same overrides relationship-same. An institution claims consent on behalf of people who never gave it. The relationship between the labeler and the lab, the artist and the model, the writer and the training set, never happened in the form of consent. The institution writes a policy that says consent was obtained because the data was publicly available, or because terms of service were technically agreed to, or because the use claimed protection under fair use doctrine. The institutional pin overrides the relational one.

This is the structural mismatch Chris Stephens named in her LinkedIn post, applied to a different room. Responsibility without authority is one face of it. Consent without agreement is another. Both are pins that the institution writes on behalf of someone who cannot push back.

The full pin

A What Do You Mean (WDYM) pin on a magnet word needs both coordinates.

Floor: Which kind of dignity is being claimed in this room? Theological, procedural, contribution, consent.

Layer: Which kind of sameness is being claimed when the word gets used? Category, descent, collectively-agreed, recognition-anchored, relationship.

The full pin reads: “In this room, dignity means [floor-specific definition], measured by [layer of sameness], with these includes, these excludes, and a date to revisit.” Without both coordinates, the room is operating on a partial coordinate, and the drift will start immediately.

Scripts

For the procedural floor: “When you say we will hold them accountable, what authority are they being given to act on what they are accountable for? If we cannot name the authority, the accountability is decorative.”

For the contribution floor: “When you say this person earned their place, whose unrecognized work made the place possible? If we cannot name that chain, the recognition is operating on a partial map.” (Think of the term, standing on the shoulders of giants).

For the consent floor: “When you say consent was given, who gave it, and could they have refused without losing what they were trying to do? If the answer is no, the consent is not consent.”

Friday tee

Eric Patterson sent me a piece by Hollis Robbins this week called “What does the ‘higher’ in higher ed mean?” Robbins is reading the word higher the way I have been reading dignity, the way Chris Stephens is reading accountability, the way Arimitsu is reading same. Same shape of failure. Different magnet word. I am holding it for Friday.

Return

Three writers in conversation across one week, and a Pope in Rome refusing the comfortable floor, and the field guide entry is provided.

The word is dignity. The floors are procedural, contribution, consent. The layers are five. The pin is both. The room is wherever you are.

Future-us is reading.

Previously, on What Time Binds: The Word Is Dignity walked Monday’s encyclical and the apology no pope had made before. Feynman’s Real Question Was “What Do You Mean?” developed the floors concept this piece builds on. What Do You Mean, “Crime Against Humanity”? ran the UN slavery vote through the same diagnostic.

Which floor is your room standing on without knowing it? Tell me in the comments.

Subscribe to What Time Binds for Friday’s Binding, the monthly Saturday Pinned Terms strip, and next week’s Monday essay.

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In 1876, the editors of Princeton’s student newspaper argued against exam proctors. Watching students for fraud was, they wrote, “a means of bad moral education.” Treat a person as presumptively dishonest, and some will oblige you; treat the same person as honorable, and they learn to behave that way. Their proposal: every student signs a pledge that they received no help, and the faculty find better work than patrolling the room.

That proposal became the Princeton Honor Code in 1893. It was modified only lightly across the next 133 years. It outlasted two world wars, the 1960s, Watergate, search engines, and SparkNotes.

This month, the Princeton faculty voted to put the proctors back.

The Honor Code is technically still in place. Students still sign the pledge. Now, a professor stands at the front of the room to confirm it is true. Rose Horowitch reported the vote in The Atlantic on May 12. The system that ran without watchers now has watchers.

I teach, and I have watched this pressure arrive in real classrooms, so the phrase is worth pinning before it loses the last of its shape.

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The word

“What do you mean by ‘the honor system’?”

You say it at work more than you think. Expense reports. Remote hours. Open-book policies. Self-certification. Self-checkout. The phrase shows up wherever an institution decides not to check.

Why it is a magnet

The phrase pulls three ways, and the three do not agree with each other.

One: trust without verification. Nobody watches, and you behave anyway.

Two: a social code held up by peers. You sign a pledge. You are expected to report the people who break it.

Three: a compliance label on a watched process. The pledge stays stapled to the top. The words “honor system” remain in the handbook.

Princeton’s faculty meant the first. The code ran on the second. AI creation forced the third. The phrase never changed through any of it. That is the magnet at work: one set of words holding three meanings, with everyone certain they share one.

The failure mode

An honor system holds only while the cost of breaking it stays high enough that most people choose not to. Generative AI took that cost to almost nothing. A unique essay, in any style, in seconds, with typos added so it reads as human.

The collapse then arrives all at once, at a tipping point. Nadia Makuc, a Princeton senior and former chair of the Honor Committee, put the mechanism in one line: visible cheating breeds more cheating. The numbers track it. The Committee on Discipline found 82 students responsible for violations in 2024–25, up from 50 in 2021–22. In a senior survey of 501 students, 30 percent said they cheated, 28 percent used a chatbot on an assignment where it was barred, and 45 percent knew of a peer cheating and said nothing. The students who follow the rule start to feel like the only ones still holding it.

The tool did not cheat anyone. It changed the price of cheating, and once the price fell the rest followed — visible violations, the belief that everyone was doing it, and an institution that answered by putting watchers back. The honor system did not die of generative AI. It died of what generative AI made cheap and what Princeton did next.

Pin it

In this room, “the honor system” means a coordination rule that holds only while three conditions are true at the same time:

1. The cost of breaking it stays real.

2. Breaking it stays visible.

3. Most people believe most people comply.

Lose any one of the three and what is left is an unenforced rule wearing the old name.

Boundary test

Includes: expense self-reporting, take-home exams, remote-work hours, open-source attribution, self-checkout.

Excludes: anything already proctored, audited, logged, or version-tracked.

Example: Princeton, 1893 to 2026.

Non-example: a proctored final with a signed pledge stapled on top. The pledge is theater once the proctor is in the room.

The one-minute script

“When you say we run on the honor system here, which part do you mean? That we trust people, or that nobody checks? Those two came apart somewhere, and I want to know which one we are relying on before I sign.”

Log it

Write down which of the three conditions your team actually depends on. Date it. Revisit it when the cost of breaking the rule drops, because something always drops it. At Princeton the something was generative AI. In your shop it will be something else, and it will arrive without an announcement.

What gets passed forward

The 1876 editors saw part of this. They warned that suspicion manufactures the rogue it claims to catch. They were right, and being right did not save the code.

The word got passed forward intact. The conditions that made it mean something stayed behind in a different century.

When you inherit a word, check whether you also inherited the conditions that made it work. “Honor system” still sounds like 1893. It now operates like a proctored room with a signature page. Same words. Different machine underneath.

Ask the question before you sign the pledge. What do you mean by the honor system?

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What Do You Mean? is a weekly field guide inside What Time Binds. One magnet word, pinned and made usable, every Wednesday.

— Jerry Washington, Ed.D. • what-time-binds.com

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On February 14, 2025, Peter Kyle stood at the Munich Security Conference and announced that the United Kingdom’s AI Safety Institute would be renamed the AI Security Institute. Same building, staff, and budget. The mandate narrowed: chemical and biological weapon misuse, cyberattacks, and child sexual abuse material. Out of scope: bias, freedom of speech.

Four months later, the United States did its version. The Commerce Department renamed its AI Safety Institute the Center for AI Standards and Innovation. Howard Lutnick said the rebrand removed “censorship and regulations” used “under the guise of national security.” NIST guidance instructed CAISI partners to drop references to “AI safety,” “responsible AI,” and “AI fairness.”

The buildings did not change, but the contents did.

If you watched the Bletchley Declaration in November 2023, the Seoul Summit in May 2024, and the Paris AI Action Summit in February 2025 in sequence, you watched the phrase “AI safety” walk into a meeting room, sit down, and discover it had been holding two conversations the whole time. At Paris, fifty-eight countries signed a Statement on Inclusive and Sustainable AI. The United States and the United Kingdom refused. JD Vance, on the dais: “The AI future is not going to be won by hand-wringing about safety.“

That moment is diagnostic. The word everyone had been using at every summit for two years did not survive: it drifted.

A Magnet Word

“AI safety” is one symptom. The bigger word is “AI governance.”

Some words pull people into different interpretations while feeling like agreement. They keep meetings moving, people signing contracts, and policies passing. Then, six months or six years later, someone opens the deliverable and discovers that the room had been using the same syllables to describe four different jobs.

Watch what happens when you pull on “AI governance.”

In Brussels, it means the European Commission’s coordinating function across twenty-seven member states under the AI Act. In Washington, until January 2025, it meant Executive Order 14110; after January 2025, it meant Executive Order 14179, which rescinded 14110 in three days. In Sacramento, it meant SB 1047 (until Governor Newsom vetoed it in September 2024); then it meant SB 53, which kept transparency reporting and dropped the kill-switch. In Denver, under the Colorado AI Act, it means a developer’s duty of “reasonable care” to prevent algorithmic discrimination. In an Anthropic Responsible Scaling Policy, it means an internal Risk Officer, a capability threshold, and a Long-Term Benefit Trust. In a Deloitte board roadmap, it means a skills matrix and a refresh schedule. At AI Now, it means antitrust and worker organizing. At UNESCO, it means human rights and dignity grounded in 194-state agreement.

These are different objects. They share a vocabulary and almost nothing else.

What I Watched For Twenty-Three Years

I spent twenty-three years inside institutions where ambiguous language carried operational consequence. Marine Corps construction sites. Logistics commands across four countries. Corporate facilities portfolios in three states. A 23-office closure managed across HR, Legal, Finance, IT, and Business Development through five years and a pandemic. The pattern showed up every time. The words looked clear. The room nodded. The cost arrived later.

In 2005, I took over the facilities portfolio for 3D Marine Logistics Group in Okinawa: 300 facilities, three million square feet, 30 locations across Japan, Korea, Hawaii, and Guam, 4,600 personnel. I reported to a Chief of Staff who used the word “ready” daily. Every commander in the chain used “ready” daily. Nobody stopped to verify that “ready” meant the same operational state to the building maintenance officer, the procurement officer, and the host-nation liaison. We coordinated as if we agreed. When we didn’t, projects slipped.

I left active duty in 2016, finished a doctorate at USC Rossier in Organizational Change and Leadership in 2023, and spent the next two years on a scoping review of 131 sources across eight disciplines: healthcare communication, aviation crew resource management, cognitive science, military operations, organizational psychology, linguistics, high-reliability organizing, and team science. The literature confirmed what I had watched in every room I had ever worked in. The default state of human communication is drift. Shared understanding takes work. The work is to catch the drift before the budget or the policy pays for it.

Eight Rooms, Eight Words

Eight communities use “AI governance.” They mean different things.

Regulators in Brussels mean rights protection through ex ante conformity assessment and member-state market surveillance. Regulators in Washington, under the current administration, mean removing barriers to American AI dominance.

Frontier-AI labs (Anthropic, OpenAI, Google DeepMind) mean an internal Responsible Scaling Policy or Frontier Safety Framework: their own capability thresholds, their own auditors, their own stopping rules.

Big Four consultants mean a board-level oversight discipline producing model inventories, third-party risk management, and ISO 42001 mappings against the NIST AI Risk Management Framework.

Alignment researchers (Bengio, Hinton, Russell, the twenty-five-author Science paper from May 2024) mean binding licensing, mandatory pre-deployment testing, and one-third of AI R&D budgets devoted to safety. Closer in shape to nuclear-style controls than to NIST-style voluntary frameworks.

Civil-society scholars (Buolamwini, Gebru, Bender, Whittaker, Crawford, Benjamin) mean redistribution of power. Antitrust. Worker organizing. Bans on weaponized facial recognition. Refusal of the surveillance business model. Sarah Myers West, in AI Now’s 2024 paper Power and Governance in the Age of AI: “AI as we know it today is a creation of concentrated industry power.”

DoD and federal contractors mean a Chief AI Officer role under OMB M-24-10, an inventory of “rights-impacting” and “safety-impacting” AI systems, and compliance with the five Responsible AI principles (Responsible, Equitable, Traceable, Reliable, Governable).

UNESCO and the African Union mean development capability, digital sovereignty, and human rights anchored in 194-state agreement. From outside the Brussels-Washington axis, “AI governance” often reads as a Western export imposed on jurisdictions that did not draft it.

Same word. Eight rooms. Eight non-overlapping objects.

Where Drift Turns Into Cost

The fault lines are where decisions actually turn.

Catastrophic-risk control versus algorithmic-discrimination prevention. Bengio, Hinton, GovAI, METR, and the Future of Life Institute treat governance as preventing extinction-class harms from frontier systems. Buolamwini, Gebru, the Algorithmic Justice League, and the Colorado AI Act treat governance as preventing harm to people who are already being injured. The two camps publicly disagree about which deserves the word.

Voluntary risk management versus enforceable regulation. NIST AI RMF, the Hiroshima Code of Conduct, the OECD Principles, and most corporate Responsible Scaling Policies are voluntary. The EU AI Act, the Colorado AI Act, NYC Local Law 144, and SB 1047 (briefly) carried teeth. The EU version comes with fines up to 7 percent of worldwide turnover. The voluntary versions come with press releases.

Innovation enablement versus power redistribution. Executive Order 14179, signed January 23, 2025, defines governance as removing barriers to “America’s global AI dominance.” AI Now’s 2025 Artificial Power report defines governance as breaking up tech oligarchy. Both call themselves AI governance. Both cite the same underlying technology. They share no policy primitives.

A vote on SB 1047 turned on which definition was operative. The Paris no-signs turned on which definition was operative. The CAISI rebrand turned on which definition was operative. When real consequences attach to a magnet word, somebody’s definition wins, and somebody’s loses, and the people whose definition lost rarely got to vote on which one was on the ballot.

Four Questions to Run in Any Room

Here is the test I run in any room where the phrase “AI governance” is used.

• Whose harm are we governing? The public’s, the company’s, the state’s, or humanity-at-large?

• Who pays if we are wrong? Users, taxpayers, shareholders, or future people?

• Who enforces? A regulator, a board, an auditor, or a market?

• Who decides what “wrong” means? Courts, voters, engineers, or executives?

If two people in the room give different answers to any of these questions, you are looking at a magnet word event. Stop the meeting. Pin the term.

The one-minute script:

“When we say ‘AI governance’ in this conversation, are we talking about (a) preventing catastrophic harm from frontier models, (b) preventing discrimination in consequential decisions, (c) ensuring board-level oversight of enterprise AI use, or (d) protecting national competitiveness? I want to make sure we are working from the same definition before we agree on next steps.”

That sentence costs sixty seconds. It saves whatever the consequence would have cost on the back end.

Governance Worth the Name

Governance worth the name names four things.

• It names the harm: who gets hurt, in what way, on what timeline, with what evidence.

• It names the actor: which institution, which person, which budget line is responsible for the decision that creates the harm.

• It names the enforcement: which regulator, court, auditor, or contracting officer can stop the harm or reverse it.

• It names the redress: what the injured party gets when the harm happens, who pays for it, and how soon.

Anything that calls itself governance while omitting any of these four is, in Emily Bender’s phrase, marketing.

Under that test, NYC Local Law 144 is governance: narrow, contested, and badly enforced (the New York State Comptroller’s December 2025 audit found 75 percent of complaints misrouted), but the four parts are present. Anthropic’s Responsible Scaling Policy, version 3.0, fails the test. It names the harm and the actor. It does not name the enforcement (Anthropic audits Anthropic) or the redress (no injured party has standing). The same is true of OpenAI’s Preparedness Framework and Google DeepMind’s Frontier Safety Framework. These are commitments. Calling them governance softens the word until it cannot do the work.

What This Means

The most precise users of the term “AI governance” right now are working from outside the rooms where it is operationalized. Bender and Hanna’s The AI Con (May 2025). Whittaker’s NDSS keynote. Buolamwini’s Algorithmic Justice League. AI Now’s Artificial Power report. They name the harms. They name the actors. They describe what enforcement and redress would look like. Do they get invited to write the policies?

The companies that draft Responsible Scaling Policies fund the academic centers that evaluate them. Consulting firms that sell governance services write the frameworks regulators cite. The U.S. and U.K. security services rebrand institutes without legislative input. The word “AI governance” sits in the middle of all of it, doing the work of agreement while the underlying definitions move in opposite directions.

If you are in a meeting next week where someone uses the phrase, ask which version they mean. If they cannot answer in one sentence, you are in a vocabulary problem pretending to be a governance conversation. Somebody is going to pay for that mistake. The only question is whether you saw it coming.

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Jerry W. Washington, Ed.D., is a twenty-three-year U.S. Marine Corps retired Master Sergeant. Ed.D., USC Rossier (Organizational Change and Leadership) and author of Simulated Realities: Generative AI and the Remanufacture of Professionalism (2023) and a 131-source scoping review on shared meaning under pressure (working paper, 2026). He’s also an independent advisor on AI readiness for education and workforce systems: K–12 districts, community colleges, workforce boards, and veteran-service organizations. He publishes weekly at What Time Binds. Engagements and writing at jerrywwashington.com.

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I started the week sketching a Pinned Terms strip about accountability with my daughter.

The setup wrote itself. Juno Tanaka stands at the whiteboard. Six people in the Ops Room. The card on the wall reads ACCOUNTABLE. Each character has a different definition loaded in their head, and none of them know it. Juno reaches up, pulls the card down, holds it in two fingers like contraband, and says, “This one’s a trench coat.”

That’s the gag. Three panels. A magnet word doing what magnet words do, a character calling it out, a small repair move written on the wall. Comic done.

Then I read Kendra Okposo’s HBR piece, “Accountability Must Be Chosen, Not Mandated,” and I closed the laptop on the strip.

The strip would still work as a strip. The problem was scope. What three panels can show needs forty minutes in a real meeting, and Okposo had already done most of the work to set that meeting up. Her case study with a global transportation provider is honest reporting. Her Mindset/Meaning/Mechanisms structure is clean. Her core finding earns the column inches HBR gave it: you cannot impose accountability, you can only build the conditions where people choose it.

I want to push on one of her three Ms: Meaning.

What Okposo means by Meaning

Okposo defines Meaning as the why that makes the choice worth it. Values like integrity. A shared goal that transcends any one person’s contribution. Care and commitment that survive when scrutiny fades.

That definition is correct, and it is doing only half the job.

There is another version of Meaning operating in the same conference room, and it breaks teams long before motivation gets a chance to do its work: the referent of the word accountability itself. Not why people should choose it. What they think they are choosing.

In a business performance meeting, six leaders agree they need to be more accountable. One means I will own the result regardless of what my function controlled. Another means I will surface bad news earlier. Another means I will stop blaming other functions in front of leadership. Another means I will deliver what I committed to or call it out before the deadline. Another means I will let myself be measured against numbers I disagree with. Another means I will do what I am told without asking why.

Six votes for accountability. Six different votes. The motivational why does not fix this, because each person is choosing a different thing. The room sounds aligned, but it isn’t.

This is the meaning failure that runs underneath the meaning failure Okposo names.

The case study is the proof

Read her case study carefully.

The transportation company’s leaders worked with BTS to “co-author a playbook” that broke accountability into “real, recognizable moments in their work, both great and not great.” Okposo lists the moments they pinned. Name the real problem under the symptom. Claim your role in the solution. Raise enterprise-level concerns even when they cross your function’s lines.

That co-authoring is the move. The leaders were not motivated into accountability. They wrote a shared definition of the word and then practiced it across three ten-week cycles. Thirty weeks of repetition mattered. So did the sequencing. The definition came first.

Okposo files this under Mindset and reinforces it under Mechanisms. Read it again, and the pinning sits in the middle: a working agreement on what accountable points to, in this company, in these meetings, this quarter. Without that agreement, the mindset shift has nothing to anchor to, and the mechanisms enforce six different things at once.

She did the work. I would name it Meaning, in the sense of shared reference, and call it the deeper repair move her clients actually executed.

Pin it in the room

If you sit on a leadership team where accountability keeps surfacing as a problem, run a forty-minute session before the next quarterly review. Pull the word off the wall. Treat it as a magnet word until the room has decided otherwise.

Three questions, written down, agreed by everyone present.

What does accountability include here? Two or three concrete behaviors. Surfacing a missed milestone within forty-eight hours. Naming what you personally controlled in the outcome. Bringing your function’s blockers to the all-hands before someone else has to.

What does it exclude here? Two or three behaviors people sometimes confuse with it. Taking blame for outcomes you did not influence. Public self-criticism that signals humility without changing behavior. Quiet compliance with a target you privately believe is wrong.

When do we revisit? Name the date. Quarterly is honest. Annual is a slow walk into the same drift.

The artifact is the agreement. It lives somewhere future-us can find it. The next leader who joins the team reads it before their first business performance review. The next time someone says “we need more accountability around here,” the room can answer: we have a working definition. Are we executing it, or do we need to revise it?

That is the conversation Okposo’s transportation company actually had. The playbook is the artifact. The thirty weeks were the practice. The Mindset shift was the result.

Back to the strip

I might still draw the comic. Juno’s trench coat line is too good to leave on the cutting room floor.

A strip can show the collision. It cannot carry the sentence the team writes after. That sentence is what holds the team together in the next quarter, the next reorganization, the next time the word accountability shows up on a slide, and everyone nods at a different meaning.

Choose accountability, by all means. First, decide together what you are choosing.

A few questions for your team

1. The last time someone on your leadership team said “we need to be more accountable,” what did they mean? What did the rest of the room hear?

2. If you wrote down the includes/excludes for accountability in your organization right now, would the document agree with the behaviors your performance reviews actually reward?

3. Who on your team has been carrying blame for outcomes they did not influence, because no one paused to define what accountability meant for that work?

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Jerry W. Washington, Ed.D., is a retired Marine Corps Master Sergeant, a UCI Division of Continuing Education instructor, and the publisher of What Time Binds. He writes about meaning, time-binding, and how teams lose and recover shared reality under pressure.

If this piece was useful, forward it to one person on a leadership team that keeps having the same accountability conversation. Then subscribe so the next one finds you.

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I researched this essay on the day it happened.

March 25, 2026. One hundred and twenty-three nations voted to declare the transatlantic slave trade the gravest crime against humanity. Three voted no. The United States was one of them.

I pulled the resolution text. I read the U.S. explanation of the vote. I read the EU’s abstention statement, the UK’s statement, and France’s separate legal filing. I mapped the magnet words, ran the vote through my framework, and started writing.

Then I closed the document.

I closed it because my hands were shaking, and I have learned, the hard way, over years of doing this work in public, to tell the difference between writing with conviction and writing with heat. I had heat. I could feel it in the first paragraph. In the way I was reaching for words that accused rather than words that explained.

So I waited. I told myself I would come back when I could do what I ask my readers to do: name my priors, hold the strongest counterargument, and let the evidence lead.

A month later, I came back. The heat is still there. I am a Black man. I am the descendant of enslaved people. I spent 23 years in the Marine Corps building infrastructure for a country whose founding documents excluded my ancestors from the category of “person.” When my country votes no on a resolution about slavery, my body has an opinion before my brain arrives.

That is my prior. I carry it. I name it here because I believe in doing the work I ask of others: examining the maps I inherited before I use them to read new terrain.

Here is where the frustration sharpens. I know my priors. I have spent years interrogating them publicly in essays, in a research framework, and in a scoping review of 131 academic sources across eight disciplines. I wrote an entire essay called “Race as a Ghost Map” that opens by admitting the emotional weight I bring to these questions and then lays out a self-audit protocol anyone can use.

The question that kept me from publishing in March, and brings me back now, is whether the people who cast that “no” vote, and the 52 nations who abstained, have done the same work on their own maps. Because the evidence from the General Assembly floor suggests they have not.

The magnet word at the center

On March 25, 2026, the UN General Assembly adopted Resolution A/80/L.48 by a vote of 123 in favor, 3 against, and 52 abstentions. Ghana introduced the resolution on behalf of all 54 African Union member states. The United States, Israel, and Argentina were the three “no” votes. Every EU member abstained as a bloc. The resolution declared the trafficking and chattel enslavement of Africans “the gravest crime against humanity” and called for reparatory justice.

The magnet word is “crime.”

For the 123 nations voting yes, “crime against humanity” carries a specific moral and legal weight. It means: this happened, it was wrong by any standard human beings have ever held, the consequences persist in measurable ways, and the international community has an obligation to address those consequences. The word “gravest” adds emphasis. The word “reparations” names the obligation.

For the three nations voting no, and for much of the abstaining bloc, the same phrase triggers a different reading entirely. “Crime against humanity” sounds like a legal instrument being applied retroactively. “Gravest” sounds like a ranking that diminishes other atrocities. “Reparations” sounds like a financial claim without a clear recipient.

Same words. Different pictures. And because nobody stopped to say, “When we say crime, do we mean a moral declaration or a legal indictment?” the entire vote became a collision of unstated definitions.

This is what I study. This is what my Meaning Repair as Cognitive Infrastructure framework maps. And this UN vote is that framework running at full civic scale.

The four phases, live from the General Assembly

Drift. The meaning of “crime against humanity” has been drifting for 25 years. The 2001 Durban Declaration called slavery “a crime against humanity and should always have been so,” a carefully crafted phrase that acknowledged the moral reality while sidestepping retroactive legal application. That formulation was already a compromise. It contained two competing definitions inside a single sentence: slavery is a crime (present tense, moral), and it should always have been (subjunctive, acknowledging it was not legally designated as such at the time). For a quarter century, that ambiguity held. Both sides could point to the same words and see their own meaning reflected back. Drift felt like agreement.

Suppression. Four consecutive U.S. administrations boycotted Durban follow-up events: Bush, Obama, Trump, Biden. Every boycott is a suppression move at institutional scale. Drift was detected. The mismatch between the moral reading and the legal reading was known. And the response, each time, was to leave the room. The research on suppression in my scoping review describes this pattern precisely: silence spreads through storytelling. Each administration inherited the precedent of the last administration’s exit, and the bar for re-entry rose. By the time Ghana introduced this resolution in March 2026, the U.S. had 25 years of accumulated suppression infrastructure, inherited silence dressed as diplomatic continuity.

I wrote about this mechanism in “Version Control for Democracy.” At the President’s House in Philadelphia, NPS employees removed interpretive panels about enslaved people from a jointly curated historic site without notice or consultation. Judge Rufe’s ruling used version-control language to describe the damage: public memory had been edited by power without process. Her order was a restore command.

The UN vote is the same pattern at global scale. For 25 years, the Western bloc has been editing the shared record by subtraction: leaving conferences, boycotting commemorations, abstaining from votes. Each exit removes a panel from the interpretive exhibit. The simplified story fills the gap on its own.

Repair Activation. Ghana’s resolution is itself a repair move. Foreign Minister Ablakwa walked to the General Assembly podium and, in effect, said: “We have been using the same words and meaning different things for a quarter century. Here is what we mean. Here are six specific components: apology, restitution, compensation, rehabilitation, satisfaction, and guarantees of non-repetition. Here is the legal standard we are invoking: jus cogens. We are pinning this term.”

That is term pinning. It costs more than 20 seconds at this scale, and the mechanism is identical to what happens in a meeting room when someone finally says, “When we say finalize, do we mean the spec is locked, or the date is confirmed?”

Outcome. The pin held for 123 nations. For the rest, it failed. And now future-us inherits two competing artifacts: a General Assembly declaration endorsed by a supermajority, and a formal U.S. explanation of vote that reads like a counter-definition. The drift between these two maps is wider now than before the vote, because the vote forced the incompatible definitions into the open. Successful repair in part of the room, failed repair in the rest. The feedback loop is running.

A coalition of scholars and reparations advocates issued a warning in the weeks following the vote that names the risk precisely: the resolution could stall at declaration, with reparations language diluted into development assistance, reduced to symbolic gestures, and deferred indefinitely through what they called “death by commission.” In framework terms, that is the Phase 4 danger: the pin held on the day of the vote, and the definition can still drift back if the institutional follow-through stalls.

My priors, on the table

Here is where I need to slow down and do the thing I ask readers to do.

I carry priors into this analysis. I am the descendant of people who were property under American law. My family history contains gaps that will never be filled because enslaved people were recorded as inventory, not as persons. When Ambassador Negrea said the resolution’s supporters were engaged in “cynical usage of historical wrongs as a leverage point in an attempt to reallocate modern resources to people and nations who are distantly related to the historical victims,” my body read that sentence before my mind parsed it. The phrase “distantly related” landed in a place where reason does not live first.

So I will name what I notice in myself:

I notice that I want the U.S. to be wrong. That is a prior, and I hold it.

I notice that I feel the word “distantly” as erasure, as if the distance between me and my great-great-great-grandmother, whose name I will never know because no one recorded it, is a matter of diplomatic convenience. That is an emotional response, and it carries real information; it is a signal, and I treat it as one.

I notice that when Negrea said, “President Trump has done more for Black Americans than any other president,” I stopped reading his argument and started reading his credibility. That is a suppression risk in my own processing. If I dismiss the entire U.S. position because one sentence offended me, I am doing the same thing I critique: letting the emotional charge of a magnet word shut down the grounding check.

So I went back and read the full explanation of vote. I read it as if it were a team member’s position in a high-stakes meeting where I disagreed with the conclusion and needed to understand the reasoning.

The legal core of the U.S. argument rests on rejecting retroactive application of international law. The position holds that acts from the 15th through 19th centuries cannot constitute violations of jus cogens as that term is understood in contemporary international law. This is a real argument. It has legal weight. Scholars disagree about it, and reasonable people can land on different sides.

The argument about ranking atrocities, that calling slavery the “gravest” crime, objectively diminishes the suffering of countless victims and survivors of other atrocities,” is also a real concern. Israel shares it. The EU shares it. The anxiety about creating a hierarchy of suffering is genuine, even if I believe it misreads the resolution’s intent.

I can hold these things. I can acknowledge the legal argument while believing, with everything in my body and my training, that the moral answer is clear and that the legal objection functions, in practice, as a mechanism for avoiding the moral conclusion.

The thing that breaks the repair loop is the refusal to examine one’s own priors with the same rigor.

The frustration: unexamined maps on the Assembly floor

In my “Race as a Ghost Map” essay, I wrote that shaming people rarely updates maps. Ghost Maps thrive in defensiveness. If we want different outcomes, we have to get better at the craft of updating.

I still believe that. And I have tried to live it: auditing my own assumptions publicly, inviting readers to do the same, building a framework grounded in evidence rather than accusation.

This vote tested my patience with that posture. Because what I saw on the Assembly floor was a room full of nations with deeply unexamined maps, maps inherited from the same era that produced the slave trade itself, defending those maps with procedural language while 123 nations asked them to do one thing: say the full sentence.

The EU abstained because the resolution used superlatives. France warned against “pitting historical tragedies against each other.” The UK said slavery must “never be forgotten” while insisting there is no legal duty to address it. These are institutional suppression moves. Drift detected. Repair declined. Room exited.

The U.S. explanation of vote went further. It deployed a “distantly related” frame that erases the living, measurable consequences of slavery: wealth gaps, health disparities, incarceration rates, educational access, as if 400 years of compounding disadvantage disappears when you add enough generations to the denominator. And it wrapped a partisan claim about presidential records inside a diplomatic statement to the General Assembly.

Ambassador Negrea’s office did not interrogate its own priors. It performed them.

And there is a deflection gaining traction in the month since the vote that deserves attention: the claim that “Africans sold Africans,” offered as if it dissolves the case for reparations. This is another magnet word at work. “Participation” is collapsing the distance between selling war captives into a regional system that predated European contact and building a four-century industrial apparatus of chattel enslavement across three continents, capitalized by state-chartered corporations, enforced by national navies, and codified into property law. As one analysis published this month in the Mail & Guardian put it, no African kingdom built the legal infrastructure of chattel slavery or created the plantation economy and the financial instruments that sustained it. The “Africans sold Africans” argument functions the same way “distantly related” functions: it lowers the resolution on the map until the uncomfortable structural detail disappears.

That is the distinction I keep coming back to. I am willing to say: here is my map, here is where it might be wrong, here is the emotional weight I bring, and here is my evidence. The frustration, the real, bone-deep frustration, is directed at institutions and actors who refuse to do the same work. Who treat their inherited position as the ground state and every challenge to it as an intrusion.

In Ghost Map terms, that refusal has a name: self-exemption drift. The belief that your map requires no audit because it arrived before you did. Everyone else has priors. You have principles.

The democracy connection

This vote did not happen in a vacuum. It landed inside a measurable democratic recession.

I have written about this pattern across multiple essays, in “Greenland, Venezuela, and the Time-Binding Collapse,” in the Reclaiming Dr. King piece, in “Version Control for Democracy.” The through-line is consistent: when democratic institutions weaken, the space for meaning repair shrinks. Autocratizing systems compress the room for the question “What do you mean?” because that question, by its nature, distributes power. It gives the person hearing a word the right to ask whether the person speaking it meant what they think it means. That is a democratic act. It requires the assumption that both parties in the exchange have standing.

The UN vote maps onto this pattern. The 123 nations that voted yes were performing a democratic function at the international level: we disagree about this term, so we will define it, vote on it, and record the result. The three nations that voted no, and the 52 that abstained, were performing a different function: we will decline to participate in the definition process while retaining the power to act as if our definition is the default.

That asymmetry is what autocratization looks like in meaning systems. The powerful party does not need to win the argument. It needs to leave the room and continue operating under the old map as if the vote never happened.

In my time-binding language, this is space-binding behavior: the mindset of territorial control in the present moment, at the expense of the intergenerational record. The resolution asked the international community to bind time, to declare that what happened between the 15th and 19th centuries carries obligations into the present and future. The “no” vote said that time is over, and its claims expired with it. The Korzybskian Gap, between our capacity to transmit knowledge across generations and our willingness to act on what that knowledge demands, is right here, on full display, in a General Assembly chamber with 193 seats.

What would repair look like?

I am a meaning repair researcher. I do not get to diagnose the drift and then walk away. That would make me guilty of the same suppression I am critiquing.

So here is my attempt at pinning the terms in this room:

“Crime against humanity,” in this context, means: a moral designation that carries present-day obligations. It includes the recognition that the consequences of the transatlantic slave trade continue to structure economic, health, and social outcomes for people of African descent worldwide. It excludes the claim that legal instruments must be applied retroactively to create criminal liability for individuals. It includes the obligation of institutional accountability. Boundary test: if a nation accepts that slavery was a “crime against humanity” and refuses any form of reparatory engagement, the moral designation is decorative.

“Reparations,” in this context, means: a structured, multi-component response that may include formal apology, institutional reform, educational initiatives, restitution of cultural property, economic investment, and policy change. It includes truth-telling. It includes the acknowledgment that “we have addressed this” requires evidence, not assertion. It excludes the reduction of all reparatory claims to cash transfers from one government to another. Boundary test: if the only version of reparations you are willing to discuss is the one you find easiest to reject, you are not discussing reparations.

“Gravest,” in this context, means: a description of scale, duration, and structural consequence, supported by the historical record of a system that operated across four centuries, three continents, and tens of millions of human lives, with institutional legacies that remain measurable. It excludes the claim that other atrocities are diminished by this designation. Boundary test: if your objection to calling slavery the “gravest” crime is that it might minimize the Holocaust, ask yourself whether you raised the same objection when the Holocaust was described in equivalent terms. If you did not, the concern is selective.

The sentence I am trying to say

I am offended by the vote. I have earned the right to be, and I am not going to pretend otherwise.

I am also trained enough, and honest enough with myself, to know that my offense is a signal, and signals inform decisions; they do not make them. It tells me where my identity is fused to the question. It tells me where I need to slow down and check my own maps. And I have done that, here, in public, the way I always try to.

What I am asking, from the U.S., from the EU, from every nation that abstained, is the same thing I ask from teams in my workshops, the same thing I teach in my course, the same thing the MRCI framework installs as a daily practice:

Say what you mean. Then check whether the person across the table heard what you intended.

Because “crime against humanity” is wearing a trench coat. It is carrying at least three different definitions into every room it enters: a moral acknowledgment, a legal designation, and a call to action. The 123 nations that voted yes tried to pin all three. The U.S. rejected all three. And the 52 who abstained declined to choose.

That is meaning failure at civilizational scale. And meaning failure, left unrepaired, compounds. It always does.

I know my priors. I have named them. I have subjected them to evidence, to counterargument, to the discomfort of admitting where my emotional response might outrun my analysis.

The question I carry out of this vote is the same one I carry out of every MRCI analysis: are you willing to do the same? Or does your map arrive pre-audited, inherited, defended, and exempt from the scrutiny you demand of everyone else’s?

Because if your answer is the second one, the drift will keep compounding. And future-us will inherit a map with the hard parts subtracted, the bolt holes still visible where the panels used to hang, and no restore point in sight.

There is a date on the horizon that makes this concrete. September 2026 marks 100 years since the 1926 League of Nations Slavery Convention, the first international treaty to ban slavery. The African Union has designated 2026 through 2036 as the Decade of Action on Reparations. Ghana’s strategy is visible now: secure the General Assembly declaration first, then use it as institutional backing for binding mechanisms. The two competing artifacts I described, the 123-nation declaration and the U.S. counter-definition, will collide again, in a more specific venue, on a shorter timeline. Future-us is already arriving.

The fix, as always, starts with four words.

What do you mean?

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Previously in this series: Version Control for Democracy explored the same meaning-repair pattern at the President’s House in Philadelphia. Race as a Ghost Map examined the cognitive machinery that keeps outdated maps of human difference in circulation. Greenland, Venezuela, and the Time-Binding Collapse mapped the democratic recession and the Korzybskian Gap.

The MRCI framework is introduced in full in The Research Behind What Time Binds. The term “self-exemption drift” was coined in the What Time Binds lexicon. “Trench coat,” Juno Tanaka’s term for a magnet word hiding multiple meanings, comes from Pinned Terms.

A What Do You Mean? essay — the What Time Binds series, where we examine the words doing more work than most people think in our civic life.

I wrote a version of this essay seventeen days ago, on the day the executive order dropped.

Thursday, March 26, 2026. I read the White House fact sheet. I read the full text of Executive Order 14398. I opened a document and started writing. I got about three thousand words in: the compliance officer in Northern Virginia, the career fair question, the definition that swallows itself. I thought I had the story.

Then I stopped. Something felt incomplete, maybe because the order was new and the legal analysis was thin. It seemed I was reacting to the headline, and I have spent enough time studying how words work under pressure to know that reacting to headlines is how you end up fighting ghosts.

So I sat on it. I told myself I would come back in two weeks and see what the professionals were saying: the employment attorneys, the compliance firms, the people whose job it is to tell companies what government language actually requires.

I came back this week.

What I found was worse than what I wrote 2 weeks ago.

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The question that starts everything

Here is the scene I kept ruminating on.

Thursday afternoon, March 26, 2026. A compliance officer at a defense contractor in Northern Virginia opens her laptop. There is a new executive order, signed that morning. She reads the title: “Addressing DEI Discrimination by Federal Contractors.” She reads the mandatory contract clause her company must adopt by April 25 (at the time, that was 30 days away). She reads it again.

Then she picks up the phone and calls her general counsel.

“We sponsor a table at the Howard University career fair every spring,” she says. “Is that still legal?”

Her general counsel pauses. “I need to read the definition section again.”

They are both educated people. They know what “discrimination” means. They have spent years making sure their company follows federal anti-discrimination law. The problem is that, as of that Thursday, they cannot tell whether their compliance program is now in violation.

That pause, that uncertainty, is where the story usually stops. The headline says “DEI ban.” People take sides. Supporters say it is about restoring merit, and critics say it is about rolling back civil rights. Everyone argues about the politics.

I want to take you somewhere else. I want to show you the machinery underneath, the part that does not make the headlines but does the most damage. Because there are two words buried in the contract clause that changes everything about how this order works. And it seems I am the only one talking about them.

The first word is “discrimination,” but it is not the word you think

Since the Civil Rights Act of 1964, “discrimination” has carried a stable meaning in federal contracting. It meant: treating individuals differently based on race, color, religion, sex, or national origin, in ways that deny them equal opportunity.

Under that definition, companies built diversity programs as tools to prevent discrimination. Executive Order 11246, signed by Lyndon Johnson on September 24, 1965, required federal contractors to take active steps to address employment barriers. The logic was direct: if your workforce looks nothing like the available labor pool, and you cannot explain why, something in your process is filtering people out. Find it. Fix it.

That order stood for sixty years. On January 21, 2025 (Trumps first full day in office), President Trump revoked it through Executive Order 14173, “Ending Illegal Discrimination and Restoring Merit-Based Opportunity.”

The new executive order — EO 14398, signed fifteen months later on March 26, 2026, finishes the job. It defines “racially discriminatory DEI activities” as:

Read “allocation or deployment of an entity’s resources” slowly. A mentoring program for first-generation professionals? An employee resource group? A career fair table at a historically Black university? As the law firm Lexology noted, the order “includes an expanded definition of prohibited DEI activities that could create risk around programs previously considered lawful.”

The word “discrimination” has been turned around. It now points in the opposite direction from where it pointed for six decades. The compliance officer cannot tell exactly where the new line is.

That vagueness matters. But it is the starting point, not the ending point. Because what I found when I came back to this story is that the vagueness is connected to something much more specific and much more dangerous.

The second word is the one that actually matters

When I read the law firm analyses this week, I expected to find arguments about the definition. I did. Every firm flags the vagueness. Every firm warns that the definition of “racially discriminatory DEI activities” is broader than existing civil rights law.

But the word that kept showing up in bold, in red, in capital letters, across Skadden, Arnold & Porter, Winston & Strawn, Jackson Lewis, DLA Piper, and a dozen others, was not “discrimination.”

It was “material.”

Here is the sentence in the mandatory contract clause, the one every federal contractor must sign by Friday, April 25, 2026:

“The contractor recognizes that compliance with the requirements of this clause are material to the Government’s payment decisions for purposes of section 3729(b)(4) of title 31, United States Code (False Claims Act).”

If you are not a lawyer, that sentence looks like boilerplate. It reads like the fine print nobody reads. It sounds like it belongs in the same category as “I have read and agree to the terms of service.”

It is the most consequential sentence in the entire order.

Here is what it means, in plain language: every time your company sends an invoice to the federal government (every progress payment, every milestone billing, every monthly statement), you are now certifying that you are not running any program that could be called a “racially discriminatory DEI activity.” If you are running such a program, and you submit that invoice anyway, you may have just committed fraud against the United States government.

That is what “material” does. It connects a vague definition of discrimination to a very specific, very old, very powerful law. As Winston & Strawn warned, this language “is designed to eliminate one of the most common defenses in FCA cases, the argument that the violated requirement was not material to the government’s decision to pay.”

A 160-year-old law with a bounty attached

The False Claims Act was signed by Abraham Lincoln on March 2, 1863. Its original purpose was to stop defense contractors from selling the Union Army sick mules and defective gunpowder. If a contractor lied to the government about what they were delivering, they owed triple damages.

The law still works essentially the same way. If a contractor knowingly submits a false claim for payment, they owe three times the government’s damages, plus penalties per claim. It is the government’s primary tool for fighting contractor fraud, and it has recovered hundreds of billions of dollars over its lifetime.

But here is the part that changes the game: the False Claims Act has something called a “qui tam” provision. Qui tam comes from a Latin phrase meaning “he who sues on behalf of the king.” In modern English, it means this:

Any private citizen can file a lawsuit on behalf of the federal government alleging that a contractor submitted a false claim. If the case succeeds, that citizen collects between fifteen and thirty percent of the recovery.

Any citizen. A coworker. A former employee. A competitor. Someone who reads your company’s website and notices that you have a supplier diversity program.

The potential payouts are substantial. In large government contracting cases, whistleblower shares can reach into the millions.

And the Department of Justice is actively looking for these cases. At the Federal Bar Association’s Qui Tam Conference in February 2026, a month before this executive order was signed, Deputy Assistant Attorney General Brenna Jenny, who leads the Commercial Litigation Branch in the DOJ’s Civil Division, said her office is prioritizing anti-discrimination False Claims Act cases for expedited review. She said whistleblower reports are already triggering investigations across automotive, defense, pharmaceutical, technology, and telecommunications industries.

In May 2025, the DOJ had already launched its “Civil Rights Fraud Initiative”, explicitly aiming to “utilize the False Claims Act to investigate and, as appropriate, pursue claims against any recipient of federal funds that knowingly violates federal civil rights laws.”

Law firms are already advertising for clients. One firm's website has a page titled "DEI Whistleblower Rewards," telling potential whistleblowers that they may be sitting on "one of the most valuable whistleblower claims in America." Another practice that has litigated three False Claims Act cases to the Supreme Court and recovered over a billion dollars for whistleblower clients lists DEI compliance as a top area of increased enforcement scrutiny in 2026, noting that any organization that certifies compliance to the government while operating otherwise "may face FCA exposure."

Let me say that plainly. The chain works like this:

A vague definition of “discrimination,” one broad enough to cover a career fair table or a mentoring program, gets attached to a mandatory certification clause. The certification clause contains the word “material,” which links compliance to every invoice. Every invoice becomes a potential false claim. And a Civil War-era bounty system pays private citizens to find and report the violations.

Your coworker can now get paid for reporting your company’s HBCU recruitment partnership as government fraud.

How the machine runs without anyone pulling the lever

Here is what I think most people miss about this order, regardless of where they stand on the politics.

The order does not need to be enforced to work.

It works through what I think of as a chain of reasonable fears. Each link in the chain makes perfect sense to the person holding it. Nobody is being irrational. Nobody is panicking. Everyone is doing the math.

The compliance officer’s math: I have a program that could fit the broad definition. My general counsel cannot tell me with certainty that it does not. The definition will not get clearer before the April 25 deadline. If I keep the program and we get audited, my company faces contract termination, debarment, and triple damages under the False Claims Act. If I shut the program down, the cost is one less career fair table.

She shuts it down.

The general counsel’s math: I have twenty programs that touch race in some way, mentoring, recruitment outreach, employee resource groups, and supplier diversity. I can mount a legal defense for each one. But defending even one False Claims Act investigation will cost six figures in legal fees before we get to a ruling. And the definition is vague enough that I cannot guarantee we win. The safer advice is to pause everything and wait for clarity that may never come.

He advises pausing.

The subcontractor’s math: My prime contractor is now required to report me if they know or “reasonably should know” that I am violating the clause. They are going to ask me about my programs. If I have a supplier diversity initiative, I need to explain it in writing to a company that has every incentive to flag me rather than risk their own contract. The safest move is to dismantle my program before anyone asks.

She dismantles it.

The employee’s math: I just read on a law firm’s website that I could collect fifteen to thirty percent of the recovery if I report my company’s DEI programs under the False Claims Act. My company has a Black employee resource group that gets conference room space and a small budget. Is that “allocation or deployment of resources based on race”? I do not know. But the law firm says I should call for a free consultation.

He calls.

Every person in this chain is making a rational decision based on the information available to them. Nobody needs to be racist. Nobody needs to be malicious. The machine runs on reasonable fear, and reasonable fear runs on vague definitions.

This is what I mean when I say the uncertainty is the enforcement mechanism. The order does not need a single prosecution to achieve its goal. It needs the math to work. And the math works right now, today, thirteen days before the first deadline.

The strongest argument for this order and where it breaks

I owe this section to anyone reading who thinks the order is necessary. I have spent years arguing that you have to take the strongest version of the opposing position seriously, and I am not going to stop because this one is personal.

The core claim: some DEI programs crossed the line. Some organizations set demographic targets that functioned as quotas. Some evaluated employees partly on identity characteristics rather than qualifications. Some training programs treated people differently based on their race in ways that created hostile environments. When that happens, it is discrimination under Title VII, and it was illegal before this executive order existed.

That claim has evidence. The Supreme Court found on June 29, 2023, in Students for Fair Admissions v. Harvard, that Harvard’s admissions process violated the Equal Protection Clause. Some corporate DEI programs have been challenged in court for creating hostile environments. The principle that individual merit should drive employment decisions has broad public support across demographic groups. I have discussed this before here.

I take that seriously.

And the executive order still fails on its own terms. Here is why.

If the goal were to eliminate race-based quotas and preference systems that violate Title VII, the order could say that. It could define the prohibited conduct with enough precision that a compliance officer could draw a clear line. It could create a process for getting a binding interpretation. It could distinguish between programs that create racial preferences (already illegal) and programs that expand access to opportunities, legal, effective, and standard practice for decades.

The order does none of this. As Lexology’s analysis noted, “certain activities that may previously have been viewed as compliant with civil rights laws could now be risky” under the expanded definition. Arnold & Porter flagged that the order’s own legal authority, the Federal Property and Administrative Services Act, “faces headwinds” in multiple circuit courts that have read the statute narrowly and may not support regulating contractors’ internal operations at all.

The definition is wide enough to cover mentoring programs, recruitment outreach, and supplier diversity initiatives, activities with documented business value and no legal vulnerability under existing civil rights law. The vagueness forces contractors into a choice: over-comply by dismantling effective programs, or accept risk exposure by maintaining programs that might be flagged.

When you add the False Claims Act bounty to that choice, the math tilts hard toward dismantling. A company that keeps its HBCU partnership faces potential triple damages and a whistleblower lawsuit. A company that shuts it down faces... nothing.

The strongest argument for the order is about eliminating illegal preferences. The order as written eliminates the space for legal, effective, evidence-based talent development. Those are different projects. The vague definition lets them look the same. The word “material” makes the consequences identical.

What this looks like in practice, April 25 and beyond

The first deadline hits in thirteen days. By Friday, April 25, every federal agency must insert the new clause into all contracts, with flow-down to subcontractors at every tier.

By Sunday, May 25, the Federal Acquisition Regulatory Council must issue interim guidance, meaning the clause must be in contracts a full month before the guidance explaining how to interpret it arrives. Winston & Strawn called this “a notable timing gap” that “may create implementation uncertainty in the near term.”

By Friday, July 24, every agency head must report on compliance to the Assistant to the President for Domestic Policy. The Office of Management and Budget will identify “high-risk sectors” for additional scrutiny.

And the DOJ has been directed to ensure “prompt review” of whistleblower lawsuits with a decision on whether to join each case within sixty days.

The practical effect, already visible across the law firm analyses: companies are not waiting for enforcement. They are conducting what attorneys call “DEI health checks,” privileged reviews designed to identify any program that could be characterized as a “racially discriminatory DEI activity” under the broadest possible reading. The advice, nearly universal across the firms I read, is to assume the broadest reading will apply.

Several attorneys noted something else worth paying attention to. The executive order specifically excludes sex-based programs from its definition; it targets only race and ethnicity. And enforcement authority is decentralized across individual contracting agencies rather than a single specialized office. That means different agencies may interpret the same clause differently. A program that passes muster at NASA might draw scrutiny at the Department of Defense. The compliance landscape is vague and variable.

What I want you to notice

I have written this piece without my usual framework language. No four-phase models. No Latin terms for communication breakdowns. No citations to my scoping review.

I did that on purpose, because I want anyone reading this, regardless of whether you have ever heard of my work, regardless of your politics, regardless of whether you think DEI programs are essential or excessive, to see the machinery.

Here is what I want you to notice:

The word “discrimination” changed direction, and the sentence it lives in did not change shape. That is how meaning capture works. The container looks the same. The contents are opposite. And the people reading the sentence cannot tell, in the moment, that anything moved.

The word “material” did more work than any other word in the entire executive order. It connected a policy position to a fraud statute. It converted a compliance question into a financial weapon. And it activated a bounty system that turns private citizens into enforcers. Most people who have opinions about this order have never read the sentence it lives in.

The vagueness is the product. A precise order would prohibit specific conduct and allow everything else. A vague order prohibits a category so broad that the safest response is to eliminate everything in it. The compliance officer cannot tell whether her career fair table is legal. That uncertainty is what makes her shut it down. A clear rule would not produce that result. The ambiguity is doing the work.

The bounty changes the incentive structure for everyone. Before this order, a coworker who disagreed with your company’s mentoring program could complain to HR. Now, that coworker can file a qui tam lawsuit and potentially collect millions. That does not mean every coworker will. It means every person who runs a program knows that every coworker could. The possibility reshapes behavior before a single suit is filed.

Programs will disappear before anyone is charged. That is the design. The order works through pre-enforcement chilling. By the time a court examines whether the definition is constitutional, (Arnold & Porter has identified real vulnerabilities in its legal authority) the career fair tables will already be gone. The mentoring programs will already be dissolved. The supplier diversity pipelines will already be dismantled. Winning the legal argument in 2028 does not rebuild what was abandoned in 2026.

The question underneath

I started this essay on March 26, 2026, with a compliance officer who could not tell whether recruiting at Howard University was still legal. I came back this week expecting to find that the attorneys had answered her question.

They have not. Every law firm analysis I read, from the largest firms in the country, firms that represent the companies making these decisions, says the same thing in different words: the definition is broader than existing law, the boundaries are unclear, and the enforcement risk is real.

What they have answered is a different question. They have mapped the enforcement machinery. And what that machinery reveals is that the order does not need to answer the compliance officer’s question to work. It needs the question to stay unanswered. An answered question is a line you can stand on. An unanswered question is a space you retreat from.

This is true regardless of what you think about DEI. If you believe diversity programs have gone too far, you should still want the rules to be clear, because vague rules give the government power that outlasts any single policy position. The next administration can use the same machinery for opposite purposes. If you believe diversity programs are essential, you need to understand the mechanism that is dismantling them, because the mechanism is not the headline. The mechanism is a single word in a contract clause that most people will never read.

I teach people how to ask a specific question when the words in the room are carrying more than one meaning: What do you mean?

The compliance officer in Northern Virginia needs to ask that question. Her general counsel needs to ask it. The contracting agencies inserting the clause need to ask it. The DOJ officials reviewing whistleblower claims need to ask it.

Nobody built a place for that question to land.

Thirteen days from now, Friday, April 25, 2026, the clause goes into every federal contract in the country. The word “discrimination” will mean what it has meant for sixty years, and what it has meant for seventeen days, at the same time, in the same sentence, and nobody signing the contract will be able to tell which meaning counts.

The career fair table at Howard University is a real thing that helps real people get real jobs at real companies. Whether it survives the next thirteen days depends entirely on whether the person making the decision can tell what “discrimination” means.

Right now, she cannot. And that is the point.

I wrote the first version of this on Thursday, March 26, 2026, the day Executive Order 14398 was signed. I did not publish it because I wanted to see what the legal professionals would find when they read the fine print. What they found was the word “material,” and what “material” activates is a bounty system most Americans have never heard of. The mechanism matters more than the headline. It always does.

Jerry W. Washington, Ed.D., is the founder of What Time Binds and the creator of the Meaning Repair as Cognitive Infrastructure (MRCI) framework. He is a retired Marine Corps Master Sergeant, a USC Rossier School of Education graduate, and an instructor at UCI Division of Continuing Education.

Start Module 1 of the Meaning Repair for High-Stakes Teams course — completely free, no paywall.

Previous What Do You Mean? entries: What Do You Mean, “Rally Around the Flag”? | Version Control for Democracy | The Inverse Problem

I’ve been thinking about the name “Artificial Intelligence,” and the more I sit with it, the less it holds up.

We named this technology after what we think it is — intelligent. But almost every conversation I hear about it describes what it does: it writes drafts, it builds websites, it analyzes data, it generates images, it autocompletes your sentences when you’re too tired to finish them yourself.

If we named it by what it actually does, we’d call it something like Automated Labor. Or Digital Task Completion. Or, if we’re being honest about most people’s Monday morning use case, Fancy Autocomplete That Sometimes Lies.

Drop the “Artificial” entirely, and you could call it Digital Assistance. Automated Pattern Matching. Computational Draft Generation. Each of those names is more accurate than “intelligence” — but none of them would have attracted $100 billion in venture capital, so here we are.

The name matters because it front-loads an answer to a question nobody has agreed on. Call it “intelligence,” and every conversation that follows inherits an assumption: this thing thinks. Now half the room is excited, and the other half is terrified, and both reactions are responses to the name, not the tool. The person who built a website with it in seventeen days and the manager who thinks it’s a crutch for weak employees are both reacting to the word “intelligence” — and they’re arriving at opposite conclusions from the same two syllables. Neither of them is wrong based on the information that they have.

That gap — between what something does and what we’ve decided it is — runs deeper than AI. It shows up every time a demonstration gets mistaken for a definition. And it happens in almost every room I study.

The conflation

There’s a move that shows up in conversations about technology, medicine, policy, leadership, really any domain where a concept carries weight and a demonstration carries force. Someone asks, “what is this thing?” and someone else answers by showing what it can do. A product demo replaces a definition. A capability display substitutes for an explanation. A personal success story stands in for a general claim.

The audience nods. The question feels answered. And nobody notices that the actual question — what is this thing, what does it mean, who gets to define its role — was never addressed.

This conflation — treating what something does as identical to what something is — operates quietly in almost every high-stakes conversation I study.

A surgeon demonstrates a new technique that cuts procedure time in half. Impressive. But “what can this technique do in one surgeon’s hands?” is a different question than “what is this technique’s role in standard care?” The demonstration doesn’t answer the second question. Adoption protocols, failure modes, training requirements, and patient selection criteria do.

A team lead shows a quarterly dashboard where every metric is green. The room concludes the team is healthy. But green metrics are what the team produced. Whether the team is healthy depends on questions the dashboard can’t answer: how sustainable is the pace, who is burning out, and what conversations are being avoided to keep the numbers clean.

An employee shows five ways they used AI to finish a project faster. The manager concludes the employee is dependent on a crutch. A different manager concludes the employee is a visionary. Same demonstration, opposite definitions — because neither manager is responding to the demo. Both are responding to what “AI” already means in their heads, and the demo just gave each of them permission to feel more certain.

Why this happens

Alfred Korzybski identified the root of this problem nearly a century ago. In Science and Sanity (1933), he warned against what he called the “is of identity” — the tendency to collapse a thing with its description, a map with its territory, a word with the object it points to. When someone says “AI is a game-changer,” Korzybski would flag that sentence. The word “is” performs an act of identification: it treats the label and the thing as the same. But “AI” is a label covering thousands of different tools, methods, capabilities, and contexts. Saying “AI is a game-changer” skips every question that matters: which AI, for whom, under what conditions, by what measure.

Korzybski’s broader point was that humans routinely confuse levels of abstraction. A demonstration lives at one level — concrete, specific, bounded by context. A definition lives at a higher level — abstract, general, meant to travel across contexts. When a demonstration gets treated as a definition, the concrete swallows the abstract. The specific case becomes the general rule. And the room moves forward on a foundation that feels solid but isn’t.

Psychologist Edward Thorndike identified the same pattern from a different angle. In 1920, he documented what he called the halo effect: the tendency for a strong impression in one area to color judgment in unrelated areas. Military officers who were tall and attractive were also rated as more intelligent and better leaders by evaluators who had never spoken to them. One visible trait radiated outward and shaped the assessment of everything else.

Thorndike’s finding has been replicated across domains for over a century. A 1977 study by Nisbett and Wilson showed that college students who watched a warm, friendly lecturer rated him higher on physical appearance and accent — traits that had nothing to do with his warmth. The initial impression didn’t just influence related judgments. It rewired unrelated ones.

The same pattern appears in technology. A study from the Nielsen Norman Group found that websites with high visual appeal received high satisfaction ratings from users even when the task-failure rate on those same sites exceeded 50%. Users liked how the site looked, and that impression bled into their assessment of how well it worked — even when it demonstrably didn’t work. “Beautiful” became a stand-in for “usable.” The demo replaced the definition.

Where it shows up in teams

In the conversations I study — teams under pressure, organizations making high-stakes decisions, groups trying to coordinate across different assumptions — the is/does conflation creates a specific and recurring failure pattern.

Someone shows impressive results. The room treats the results as proof of a larger claim. The larger claim goes unexamined because the demonstration felt like enough. Decisions get made. And when those decisions break down later, nobody can trace the failure back to the moment where a definition was needed, and a demo was offered instead.

Here’s how it sounds in practice:

“We rolled out the new system and productivity jumped 15% in the first quarter.” That’s what the system did. What the system is — its actual role, its fit with existing workflows, its long-term maintenance burden, its impact on the people using it — requires a different conversation.

“Our culture is strong. Look at our engagement scores.” That’s what the survey produced. What “strong culture” means on this team, in this building, under these specific conditions — that question is still open.

“AI is transforming everything. Look what I built in two weeks.” That’s what happened in one person’s hands, with one set of skills, on one project. What “AI” is — as a category, as a policy question, as a set of decisions your organization needs to make — remains undefined. And every person in the room is filling in that definition with their own assumptions, silently, while nodding at the same screenshots.

The repair

The fix is simple to describe and hard to practice, which is the definition of infrastructure.

When someone shows you an impressive result, train yourself to notice the moment your brain wants to leap from “that’s what it did” to “that’s what it is.” That leap feels natural. It feels like a conclusion. In reality, it’s a shortcut — and the gap it jumps over is where most coordination failures begin.

The question that closes the gap: “That’s a strong result. Now — what does this mean for us, in our context, with our constraints?”

That question moves the conversation from demonstration to definition. It honors the evidence without letting the evidence do work it can’t do. It creates space for the room to build a shared understanding instead of leaving with five separate private interpretations of the same demo.

A few other versions of the same move, depending on the context:

“That shows what it can do. What do we think it is — for this team, right now?”

“Impressive demo. What would need to be true for that result to hold across our full operation?”

“I can see the capability. What’s our definition of success for this, and does this demo match it?”

Each of these separates the is from the does. Each one costs about ten seconds. Each one prevents a room full of people from walking away with a shared experience and no shared meaning.

Why it matters now

We are living through a period where demonstrations are abundant and definitions are scarce. New tools produce visible, shareable, impressive outputs at unprecedented speed. Screenshots travel faster than analysis. A build log can go viral while the question “what does this tool mean for how we work?” remains unasked.

That asymmetry — demonstrations outpacing definitions — is the engine of most organizational confusion around technology. Teams adopt tools before they agree on what the tools are for. Leaders see a capability demo and assume alignment that doesn’t exist. Individuals have transformative personal experiences with a tool and can’t understand why others don’t share their certainty.

The certainty is real. The experience is real. The demonstration is real. What’s missing is the shared definition — the agreement about what this thing means in this room, for these people, under these conditions.

Until that definition exists, every person in the room is watching the same demo and seeing a different thing.

This essay is part of the What Do You Mean? series on What Time Binds, where I study what happens when people use the same words and mean different things — and what to do about it.

I’m building a 10-module course called Meaning Repair for High-Stakes Teams on this Substack. Module 1 is completely free. If the pattern in this essay felt familiar, that’s where to start: what-time-binds.com

I first watched the Richard Feynman magnet interview in 2015, late at night, the way people fall into YouTube rabbit holes when their brain refuses to shut off. I’ve always been drawn to physics, especially theoretical physics, because it demands a kind of honesty that most public discourse avoids. You don’t get to win with your feelings. You don’t get to hide behind a slogan. You have to build a coherent model.

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That’s why the magnet clip stuck with me.

The interviewer holds up two magnets and asks what most of us have wondered at some point: What is that “feeling” between them? The push. The pull. The invisible insistence.

And Feynman doesn’t answer the “why” first.

He answers the meaning first.

He pauses and says, in effect: What do you mean by “the feeling between the magnets”?
Not to be clever. Not to stall. To do the work that makes an answer possible.

That’s the part people miss. The clip is shared with the caption “Richard Feynman. Why.” I think the heavy lifting happens earlier. The moment he refuses to accept a fuzzy question and treats clarity as a form of respect.

Because if you let the question drift, the conversation drifts with it.

And that is the story of our current information environment: same words, different payloads, everyone convinced the other person is being ridiculous.

The moment he changes the room

Here’s the move.

The interviewer says, basically: There’s something there when you push magnets together. What is it?

Feynman replies: What do you mean by that?

He’s not arguing with the curiosity. He’s diagnosing the problem. “Feeling” can mean a tactile sensation in your hands. It can mean a mysterious substance in the air. It can mean a mechanism. It can mean a metaphysical “reason.”

If you don’t decide which one you mean, you don’t get an explanation. You get two people talking in different frames, passing sound through each other.

Clarity is the price of admission for “why.”

This is exactly what the What Do You Mean? series is about: the invisible infrastructure that lets people share a world long enough to think together.

And Feynman shows something else that matters right now: the emotional temperature of a conversation often rises because the question was vague, not because anyone was malicious.

One person thinks they’re asking for mechanism.
The other hears an existential demand.
Then we get heat, posture, performance.

Feynman defuses that by repairing the question before he touches the answer.

“Why” questions have floors

Once Feynman has the question stabilized, he gives a second lesson that belongs in every civic conversation: every “why” rests on assumptions you agreed to stop questioning.

He tells a story about “Aunt Minnie” in the hospital. People accept a certain level of “why” as satisfying because they share background knowledge about hips, hospitals, ice, gravity, and social roles. Change the listener (he imagines someone from another planet) and suddenly your explanation collapses. The “why” depends on a shared framework.

That’s a brutal truth about public discourse:

We often argue about “why” while hiding the fact that we don’t share a floor.

One person’s floor is “markets.”
Another person’s floor is “justice.”
Another person’s floor is “God.”
Another person’s floor is “history.”
Another person’s floor is “my lived experience.”

If you don’t surface the floor, the conversation becomes a spiraling ladder of “why” that ends in contempt.

Feynman doesn’t pretend he can solve that.

He says, plainly, that explanations bottom out. At some level, you hit fundamentals, things you take as elements of the world, at least until you have a deeper theory. In magnet talk, you run into electromagnetic force. You can go deeper if you’re trained. You still hit bedrock eventually.

It’s called epistemic hygiene.

It’s also a direct challenge to the culture of instant certainty.

The “cheating” principle

This is my favorite part of the interview, and it’s a warning label for the whole internet.

Feynman says he can’t explain magnet attraction by comparing it to rubber bands, because that would be cheating. It would sound familiar. It would feel satisfying. It would smuggle in a mechanism you haven’t earned. Then, the moment you push the analogy, you end up back at the same forces you were trying to explain.

That’s the whole modern persuasion economy.

“Like a rubber band.”

“Like a virus.”

“Like a parasite.”

“Like a war.”

“Like slavery.”

“Like Nazis.”

“Like communism.”

A metaphor that wins the room can still be cheating.

The point is not “never use metaphor.” The point is: don’t use a metaphor to avoid the hard work of mechanism. Don’t use familiarity to replace understanding.

Feynman is modeling a different ethic: if an explanation makes you feel smart while making you less accurate, it’s not an explanation. It’s a performance.

60-second tool: The Feynman Reset

Use this when a conversation starts getting heated quickly, at work, in civic meetings, in comment threads, and in family group texts.

Step 1: Pin the word.
What term is doing the most work right now?

Step 2: Name the meaning layer.
Are we stuck on definition, mechanism, scope, evidence, implication, or values?

Step 3: Ask one clarifying question and stop talking.
Let the other person repair their meaning.

Steal-this scripts:

• “When you say X, what do you mean by that in this context?”

• “What would X look like in practice?”

• “Walk me through the mechanism—how does X lead to Y?”

• “Which cases count, and which cases don’t?”

• “What would we need to see to agree we’re right or wrong?”

This is repair-by-design. You’re changing the conditions so shared reality can re-form.

Why it matters

Feynman’s magnet clip keeps circulating because it scratches an itch people can’t name: the feeling that we live in a world where everyone talks, and nobody explains.

We’re saturated with claims. We’re starved for mechanisms.

And that gap makes people easier to manipulate.

A high-compression meme doesn’t need to be accurate. It just needs to be legible in two seconds and identity-safe to share. It offers the comfort of a clean story. Feynman offers something harder: a disciplined curiosity that refuses to pretend.

He’s doing what every leader, educator, journalist, and organizer needs to do right now:

• Slow the frame down.

• Clarify the question.

• Separate certainty from understanding.

• Admit the limits of explanation without collapsing into cynicism.

This connects to a cognitive pattern that shows up in my research: people often believe they understand complex systems until they’re asked to explain the mechanism step-by-step. Then confidence drops. Sometimes extremism drops with it. That matters for civic life. It suggests a path out of ideological shouting: move from slogans to mechanisms.

No “gotchas,” just a shared experiment.

Quick Scan: copy/paste lines for real life

Use these in Slack, meetings, community forums, editor chats, and comment threads.

• “I want to make sure I’m tracking before I react.”

• “Define X the way you’re using it here.”

• “What’s the concrete example you have in mind?”

• “Which cases count as X?”

• “What’s the causal path—step by step?”

• “What would count as evidence on this?”

• “Are you implying X, or am I adding that?”

• “What outcome are you aiming for?”

• “What constraint are you optimizing for?”

• “What would change your mind?”

Carry it into your week

Pick one setting where you usually rush—work, civic discourse, or online.

Then run this field test three times before Sunday:

1. Identify the “magnet word” (the term doing the work).

2. Ask one mechanism or scope question.

3. Stop. Let the other person answer.

Track three signals:

• Did the temperature drop?

• Did the next turn clarify or escalate?

• Did you notice your own assumptions earlier than usual?

If you want, post your example as a Note. Real scenes sharpen the series.

Next: As promised in the last piece

We’ll tackle: when questions are used as weapons. How do we recognize a bad-faith “just asking questions” tactic and respond without taking the bait? How can we keep dialogue productive when questions are meant to provoke? That’s up next in the series.

A department meeting. The project lead breezes through updates, dropping an unfamiliar acronym and a vague reference to “the initiative.” Around the table, a few people exchange glances. You’re lost—did you miss a memo? You swallow hard. The director is in the room, and the last thing you want is to sound clueless. The moment to ask slips by, and the meeting moves on. You’re stuck, hoping you can bluff through the task. You glance around: nobody else speaks up either. Outwardly, everything seems fine, except that no one really knows what’s been agreed to.

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Moments like this carry heat. It’s the heat of status and social risk. It’s the flush of “I should know this” mixed with “If I ask, I’ll look incompetent.” It’s the tension of a public setting, colleagues and bosses in the room, and the clock pushing everyone forward. In this heat, asking “What do you mean?” feels like grabbing a live wire. So we nod along. We stay quiet. A simple point of confusion hardens into a silent fault line. The meeting adjourns with shaky alignment.

Sometimes silence feels safer than admitting we’re lost.

Why Asking for Clarity Feels Dangerous

Why is a clarifying question so fraught? It starts with face – our social self-image, the need to appear competent and respectable. A “What do you mean?” question can ding face in two directions at once. If I ask you to explain, it hints you weren’t clear (your face suffers). If I admit I’m confused, I risk looking ignorant (my face suffers). (In everyday terms, losing face means feeling embarrassed or humiliated.)

Context cranks up the risk. The impact of asking depends on power, relationships, and timing. We speak differently to a peer than to a CEO. Asking “What do you mean?” in private is easy; asking it of a person with more authority in front of others is another story. The bigger the power gap or audience, the more a clarification can feel like a challenge or a spotlight on someone’s mistake. Silence often wins out.

Our conversation habits reinforce this. Often, the original speaker will clarify themselves before anyone asks, an instinctive way to save face for everyone (remember Repair?). Directly asking someone to clarify breaks that norm. That’s why we tend to cushion it (“Sorry, just want to be sure I understand…”). We’re trained to avoid making others (or ourselves) look bad.

On top of that, many of us carry internal “voice” rules from past experience: Don’t question the boss in public. Don’t sound unsure, or you’ll look weak. These kick in automatically. We weigh the risk of speaking up (shame, angering someone, looking stupid) versus staying quiet. Too often, quiet wins. The normal fix for confusion (simply asking) gets vetoed by fear. The result? Meetings end with hidden misunderstandings, and discussions become parallel monologues, all because asking felt too risky.

Why We Don’t Ask: A Menu of Barriers

If we pull back the curtain, we find many reasons people hold back from asking for clarity. Here are some common barriers, each with what it feels like, what it sounds like inside, and what it costs:

• Status Risk (Looking Stupid) – Feels like: a pit in your stomach. Sounds like: “Everyone will think I’m clueless.” Cost: The clarification never gets aired, and you move forward with a shaky grasp of the plan.

• Power Risk (Challenging Up) – Feels like: tiptoeing through a minefield with a superior. Sounds like: “If I ask, will I be seen as second-guessing them?” Cost: Deference beats understanding. Bad ideas go unchecked; good ideas get lost in miscommunication.

• Identity Risk (Sensitive Territory) – Feels like: heart pounding at a loaded phrase. Sounds like: “If I ask what they mean, they’ll think I’m attacking their beliefs.” Cost: People talk past each other, each using the same words differently. Conflict flares over false assumptions.

• Audience Risk (Public Performance) – Feels like: red cheeks, aware that others are watching. Sounds like: “I can’t ask that in front of everyone—I’ll look dumb.” Cost: Discourse stays surface-level. Everyone nods along, and confusion goes underground until it explodes.

• Time Risk (Keep Moving) – Feels like: a spike of anxiety as the meeting races on. Sounds like: “No time to clarify—just decide and move on.” Cost: A false consensus. The team “decides” without true alignment, and time saved now is time lost fixing problems later.

• Norm Risk (Culture of Silence) – Feels like: walking on eggshells in a “no questions” culture. Sounds like: “Here, asking for clarification equals dissent.” Cost: An echo chamber. Questions are frowned upon, so misunderstandings accumulate and innovation stalls.

(Notice: personal history can amplify all these fears—a nervous temperament or past punishments make any question feel riskier. And in some settings, a general cynicism (“questions are just traps”) makes even sincere clarification feel suspect.)

These barriers often pile on. Imagine asking a powerful figure to clarify a politically loaded point in a high-stakes forum. Most of us would rather bite our tongue. Misunderstandings persist, bad decisions go uncorrected, and trust erodes.

Unasked questions turn into hidden problems.

Counter-Moves: How to Lower the Threat and Ask Anyway

Even in a high-heat moment, you can ask for clarity in a safer way. Here are a few counter-moves that reduce the threat. Each comes with a “steal-this” script:

• Stance First (Declare Intent): Preface your question with why you’re asking, to show goodwill. For example: “I want to make sure we’re on the same page. Can I ask a quick clarification about X?”

• Offer a Guess (Candidate + Escape Hatch): Give your best guess of their meaning, and invite correction. For example: “When you mention X, do you mean Y? (If I’m off, let me know.)” This way, if you’re wrong, you’ve made it easy for them to adjust without feeling challenged.

• Pinpoint the Gap (Targeted Trouble): Ask about a specific part of what they said, not the whole thing. For example: “Sorry, which initiative are we referring to, exactly?” Focusing on the unclear term keeps it neutral and contained.

• Blame the Lingo, Not the Person (Depersonalize): Frame it as clarifying a term, not questioning them. For example: “The term ‘X’ can mean different things; can we clarify how we’re using it here?” Now you’re teaming up to clarify language, rather than putting them on the spot.

• Buy Time for Clarity (Pause the Rush): If things are moving fast, propose a tiny pause to define terms. For example: “Can we take 30 seconds to clarify Y before we decide? It might save us time later.” This signals that your aim is shared understanding, not delay.

60-Second Tool: Should I Ask?

If you’re hesitating, run this mental checklist:

1. Name the barrier. Are you afraid of looking dumb (status)? Of who’s listening (audience)? Of angering someone (power)? Pinpoint the fear.

2. Choose a safe approach. Match a strategy to the fear. If it’s about tone or respect, lead with your intent (“I want to understand…”). If it’s about power dynamics, make the question extra polite or offer a guess (“Are you saying X?”). If it’s a sensitive topic, focus on the term, not the person (“Can we define Y?”).

3. Ask and stop. Pose one clear question and then listen. Don’t ramble or stack multiple questions. A concise, neutral question is far less threatening, and it invites a clear answer.

That’s it: identify what’s stopping you, adjust your approach, and ask. A few seconds of courage can prevent hours of confusion.

Quick Scan: Ready-to-Use Clarification Lines

• “I want to understand before I react…”

• “When you say X, do you mean Y?”

• “Which cases count?”

• “Walk me through the mechanism.”

• “What would count as evidence here?”

• “Are you implying X, or am I reading that in?”

• “What outcome are we aiming for?”

Each of these phrases gets clarity without casting blame. They’re short, sincere, and focused on the idea, not the person. Feel free to adapt them to your voice. The goal is to have a few go-to moves so that when confusion hits, you’re prepared to respond instead of freezing up.

Carry It Into Your Week: Clarification Challenge

Try a simple experiment this week. Use one of the quick-scan scripts above three times: maybe once in a meeting, once in a personal conversation, and once online. Each time, observe what happens. Does the temperature of the conversation drop a bit? Do you gain clarity or uncover a key detail? Does the dialogue shift in a positive way?

At week’s end, see what changed (if anything). You might be surprised how often a single clarifying question can redirect a discussion. If you try it, consider sharing your observations in the comments or Substack Notes; we can all learn from the stories.

Next: When Questions Get Weaponized

We’ve focused on asking in good faith to improve understanding. Next, we’ll tackle the flip side: when questions are used as weapons. How do we recognize a bad-faith “just asking questions” tactic and respond without taking the bait? How can we keep dialogue productive when questions are meant to provoke? That’s up next in the series.

When conversations heat up, reality has a tendency to split. A simple misunderstanding can snowball into two people talking past each other, each convinced they’re “right” and the other is being obtuse or hostile. If you’ve ever watched a minor disagreement turn into a full-blown argument in minutes, you know how easily shared reality can fracture under pressure. Words that were meant one way get heard another way. Clarifying questions start to feel like personal attacks. Before you know it, it’s no longer a dialogue – it’s two dueling monologues in separate realities.

It doesn’t have to go that way. Shared reality doesn’t maintain itself. We maintain it — one repair at a time. When the room gets hot, a few simple protocols can keep everyone on the same page. Think of these as specialized moves for asking “What do you mean?” without fanning the flames. They’re designed to lower defensiveness, untangle confusion, and get you and the other person seeing the same picture again. Here are three protocols to help re-sync understanding when it matters most:

Protocol 1: State Your Intent Before Your Question

Summary: Lead with a reassuring preface to signal curiosity, not accusation. Under stress, people often perceive a clarifying question as a challenge or attack. By stating your intent upfront, you disarm that reflex. Essentially, you’re saying, “I’m asking because I want to understand, not because I’m trying to score points.”

Why it helps: This extra sentence lowers the threat level. It assures the person that you’re not setting a trap or judging them – you simply want clarity. In psychological terms, you’re giving them a safe space to explain themselves rather than putting them on the defensive. The question that follows is far more likely to land as curiosity rather than accusation.

Example:
A: “We can’t keep extending the deadline. This team needs to be more disciplined.”
B: (feeling a flare of anxiety and potential offense) – “I want to make sure I get your point before I react. When you say ‘be more disciplined,’ what do you mean in this context?”

In this scenario, Person B first assures A that their goal is understanding, not pushback. By prefacing the question with “I want to make sure I get your point before I react,” B shows respect for A’s perspective. The follow-up question (“What do you mean by ‘be more disciplined’ here?”) then comes across as a genuine ask for clarification. A, sensing B’s good-faith stance, is more likely to explain rather than get defensive. This protocol interrupts the typical fight-or-flight pattern and buys both parties time to recalibrate rather than escalate.

Protocol 2: Offer a Hypothesis (with an Escape Hatch)

Summary: Instead of bluntly asking for meaning, paraphrase what you think they mean and invite correction. This is a two-part move: (1) Candidate interpretation – you gently hypothesize their meaning, and (2) Escape hatch – you explicitly give them room to correct you if you’re wrong. The format is something like: “When you say X, do you mean Y? If not, I’m curious what you do mean.”

Why it helps: First, offering a guessed meaning shows you’re actively trying to see it from their side. It’s a sign of good faith – you’re meeting them halfway by attempting to articulate their point. Second, the “if I’m wrong, let me know” part (the escape hatch) ensures they don’t feel cornered. You’re making it clear that you’re not wedging them into a yes-or-no trap; you’re open to being corrected. This turns a potential confrontation into a collaboration: now you’re two people refining a meaning together, rather than adversaries.

Example:
A: “I’m just so fed up with our department’s support on this project.”
B: “Let me check if I’m tracking: when you say ‘support,’ do you mean you feel the other team isn’t responding enough? If that’s not it, what do you mean exactly by ‘fed up with support’?”
A: “Yeah, exactly – I feel like whenever we ask for help, it takes days to get a reply. I don’t mean they’re bad at their jobs; I mean we’re low priority for them and it’s frustrating.”

Here, B tested a reasonable interpretation of A’s complaint. By phrasing it as “Do you mean X? If not, correct me,” B showed humility and a sincere effort to understand. A, now assured that B is not twisting their words, clarified the real issue (slow response times, not personal incompetence). Notice how this protocol flips the script from potential argument to joint problem-solving. A feels heard, and B gains a more precise grasp of A’s concern. The escape hatch (“if that’s not it...”) kept things low-pressure, giving A freedom to say “No, that’s not what I meant” without feeling judged.

Protocol 3: Zoom In on the Specific Gap

Summary: Aim your question at the specific piece that’s unclear, instead of saying a blanket “What do you mean?” Identify what type of misunderstanding might be happening and ask about that. Are you unclear on who they mean, which case they mean, or how something works? Pinpoint it. In practice, this means using a targeted prompt: “Who specifically?” “Which part are we talking about?” “How would that work?” – whichever gets to the heart of your confusion.

Why it helps: A generic “What do you mean?” can be taken as “You’re not making sense.” It’s open-ended and might put someone on the spot to justify themselves broadly. In contrast, a pointed question shows that you’re zeroing in on the idea, not the person. It also narrows the scope, making it easier for them to clarify one thing at a time. We often have trouble in conversation because one term or reference was fuzzy; by isolating that, you prevent the entire exchange from derailing. In essence, you’re debugging the conversation: finding exactly where the understanding broke and fixing just that piece.

Example:
A: “They really dropped the ball on this one. It’s honestly unethical.”
B: “I’m with you that something’s off. Quick clarification: when you say ‘they,’ who do you have in mind? And what part strikes you as unethical?”
A: “By ‘they’ I mean upper management, not the frontline team. The unethical part is that the decision was made without consulting those it impacts – it feels deceitful.”
B: “Got it – so the concern is about top leadership making secret calls that hurt the rest of us.”

In this exchange, B didn’t ask a vague question; instead, B pinpointed two potential gaps: the referent (who is ‘they’?) and the value judgment (which aspect is ‘unethical’?). This targeted approach helped A refine their statement: A specified the group (“upper management”) and the issue (lack of consultation). B could then reflect back the clarified meaning. By zooming in on ambiguous words and asking directly about them, you restore mutual understanding on precise points. It also shows respect – you’re engaging with what they actually said, not what you might have assumed they meant.

Pocket Summary

• Lead with intent: Before a tough question, reassure the other person why you’re asking. A line like “I want to understand before I respond” signals goodwill and lowers defensiveness.

• Paraphrase and ask: Try a candidate interpretation of their point, and invite correction. “Do you mean __? If not, let me know what you do mean.” This shows you’re trying to get on their wavelength, not catch them out.

• Get specific: Pinpoint the unclear element – whether it’s who a pronoun refers to, what a term encompasses, or how a process is supposed to work. Ask that specific question. It’s easier to answer and less likely to be misread as an attack.

• Goal: Keep the conversation in a shared reality by actively repairing meaning gaps. The point isn’t to win or be right in the moment, but to make sure both of you are actually talking about the same thing. Only then can real resolution or progress happen.

Field Test: Carry It into Your Week

These protocols are skills – and like any skill, they get stronger with practice. This week, look for an opportunity to test them out in real life. It doesn’t have to be a five-alarm conflict; even a minor misunderstanding or a confusing remark from a friend is a chance to try a shared reality move. For example:

• At work: The next time a discussion in a meeting starts feeling tense or convoluted, jump in with a Protocol 1 move. e.g. “Hey, before we go further, I just want to make sure I understand your perspective. You’re saying ___, is that right?” Notice how setting your intent changes the tone of the response you get.

• At home: If a family or roommate conversation hits a bump, practice Protocol 2. Paraphrase what you think they mean, and add “Correct me if I’m wrong.” You might say, “So, you’re upset because it sounded like I dismissed your idea – is that it? If not, help me understand.” Pay attention to their reaction; do they seem relieved to clarify rather than argue?

• Online or text: The next time you read a message that rubs you the wrong way, instead of firing back or stewing, try a Protocol 3 approach in your reply. Ask about a specific word or ask for an example: “When you said ____, did you have a particular situation in mind?” This can prevent an all-caps comment war by steering the exchange toward specifics and away from assumptions.

Treat these experiments as low-stakes training. You’re building your “clarification muscle memory” so that when high-stakes pressure does hit, you’ll be more comfortable using these tools naturally. The field test is simple: step into a moment of potential misunderstanding and consciously apply one protocol. Afterwards, reflect: Did the conversation feel different? Did tension ease? Each time you do this, you’re reinforcing a powerful habit. You’re proving to yourself (and others) that even under pressure, you can choose accuracy over adrenaline, alignment over ego.

Carrying These Protocols Into Your Week

You’re putting out fires all day, and you’re preventing them. You’re helping create a culture (at work, at home, in your friend group) where asking “What do you mean?” is welcome and normal, not a cause for offense. Little by little, you’re keeping shared reality intact when it matters most, one clarification at a time. And that skill compounds – after all, big conflicts are often just small misunderstandings that went un-repaired. This week, see what shifts when you take the initiative to repair in the moment. You might be surprised how much smoother things go when everyone’s operating in the same reality.

I’ve written about this pickup story before. I used it in a piece about “ghost maps,” the private mental maps we keep using long after the territory has changed. I’m bringing it back here because it’s the cleanest example I know of repair in the wild: the moment a relationship could have slid into blame, and a single clarification rebuilt the world we were sharing.

This series is about the question “What do you mean?” as cognitive infrastructure.

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Part 1 laid down the architecture: pragmatics, speech acts, implicature, the machinery of meaning when things are calm. Repair is the same machinery under pressure. It’s what keeps a conversation from splitting into two different realities.

Repair is the moment you choose accuracy over adrenaline.

The elevator that wasn’t there

I was picking up my youngest daughter after an event.

I parked where I always parked—near the elevator. The usual spot. The spot my body could find without thinking.

She wasn’t there.

Minutes passed. I called. No answer. I called again. When she finally picked up, my tone came out stern, frustrated, and certain she was being difficult. I was already halfway into a story: She’s ignoring me. She’s giving me attitude. She’s making this harder than it needs to be.

Then she said, calmly and genuinely confused:

“There is no elevator here.”

That sentence did three things in an instant:

1. It contradicted my certainty.

2. It exposed my hidden assumption: I had treated my map as the map.

3. It forced a reset: I stopped judging her behavior and started checking reality.

Nobody had told me the pickup spot had changed. I was in the wrong place. She wasn’t disrespectful. She was exactly where she was supposed to be.

I didn’t need a better argument. I needed a better alignment.

Misunderstanding doesn’t always sound like confusion. Sometimes it sounds like confidence.

That pivot, moving from posture to recalibration, is what this essay is about.

What repair is

Repair is a system built into ordinary conversation. It’s the set of moves humans use to fix problems in speaking, hearing, and understanding while the conversation is still happening.

Repair is not rare. It’s constant. You can hear it in the smallest moments:

• “Wait—who?”

• “When you say they, who are you talking about?”

• “Do you mean midnight as in 12:00 AM, or end-of-day midnight?”

• “Hold on. I’m not tracking. What do you mean?”

Repair is how we keep a shared world stable enough to think together.

Conversation isn’t a pipeline. It’s a coordination problem. Repair is the coordination tool.

How conversation analysts figured this out

The most important thing conversation analysts did was stop relying on after-the-fact explanations.

They recorded real talk: phone calls, dinner table discussions, workplace moments—and they watched what people did, turn by turn. Their basic logic is simple:

• If you want to know what someone understood, don’t guess.

• Look at what they do next.

The next turn shows how the previous turn landed. If the next turn treats the prior turn as an insult, a promise, a joke, a threat—that treatment becomes visible in the interaction itself.

That’s why repair matters so much. It’s what happens when the “next turn” reveals that two people are no longer standing on the same ground.

The preference for self-correction

Here’s a key finding that changes the way you hear conflict:

Conversation is built to let people fix their own meaning.

If a problem shows up, the structure of talk typically gives the original speaker first rights to correct, clarify, adjust, or restate. That matters because direct correction often pulls rank. It can trigger defensiveness. It can feel like a status move. It can feel like a verdict.

Repair does something smarter. It slows the interaction down and gives the other person room to steer their own meaning back into clarity.

Correction grabs the steering wheel. Repair taps the brake and hands the wheel back.

That’s one reason “What do you mean?” can be so powerful. When it works, it invites self-correction without humiliation.

When it fails, it sounds like prosecution.

So the question isn’t whether we repair. The question is whether we repair in a way that the other person can hear.

The repair ladder

Not all repair moves are equal. People typically climb a ladder from vague to specific, depending on what kind of trouble they’re dealing with.

Here’s the ladder in plain language, with examples you’ll recognize.

1) Open trouble: “Huh?”/“What?”

This signals a problem, but it doesn’t specify what kind.

A: “We should move it to the other one.”
B: “Huh?”
A: “The other meeting—the Thursday slot.”

2) Targeted trouble: “Who?”/“Where?”/“When?”

This points to a missing reference.

A: “They already decided.”
B: “Who’s ‘they’?”
A: “The committee.”

3) Repeat or partial repeat

This often puts pressure on the trouble spot without turning it into a fight.

A: “It’s obvious.”
B: “Obvious?”
A: “To me it is—because I’ve been tracking it for months.”

4) Candidate understanding: “You mean X?”

This is one of the most useful moves you can learn. It’s fast when you’re close. It’s respectful when you’re wrong and give an escape hatch.

A: “This policy is about safety.”
B: “When you say safety, do you mean public safety… or political safety?”
A: “Public safety. I’m talking about harm reduction.”

5) Meaning trouble: “What do you mean?”

This signals: I heard the words. I can’t map them into a stable interpretation.

A: “People just need to take responsibility.”
B: “What do you mean by responsibility here?”
A: “I mean ownership of the consequences, not punishment.”

“What do you mean?” is the repair move that targets the meaning layer.

It’s the move you reach for when the conversation has started producing heat because the interpretation has drifted.

Why repair breaks under pressure

Repair is cognitive, and it’s social.

A repair question can land as:

• curiosity, or

• accusation.

The difference often has nothing to do with the words and everything to do with the implied stance: Are you inviting me to clarify, or are you setting me up to lose face?

Under stress, people treat questions as evaluations. In high-conflict environments, questions get read as traps. Online, questions get weaponized so often that a sincere request for meaning can feel like a disguised attack.

Add one more ingredient: identity.

When someone’s claim is fused to their moral standing, a repair request can feel like a challenge to who they are, not what they mean. That’s how a conversation about a word turns into a fight about worth.

When identity takes the wheel, repair feels like disrespect.

And this is where my elevator story matters. I wasn’t “confused” in that parking garage. I was certain. My certainty was doing identity work: I’m the responsible one. I’m the parent. I’m the one being disrespected.

Repair took one sentence to interrupt that storyline: “There is no elevator here.”

It didn’t shame me. It reoriented me.

Repair as civic infrastructure

If you zoom out, repair becomes a public problem.

A lot of public discourse now runs on compressed language—short posts, clipped clips, headlines without context, slogans standing in for arguments. Words like “freedom,” “DEI,” “patriotism,” “violence,” “law and order,” “racism,” “woke,” and “accountability” carry dense moral payloads. People use the same vocabulary while operating different internal models.

When repair disappears, we get predictable outcomes:

• We argue against meanings that were never intended.

• We assign intent where there’s ambiguity.

• We moralize what is often a coordination failure.

• We escalate because we feel unheard, not because we’re “right.”

Repair keeps disagreement from turning into theater.

The collapse of repair is one way a society loses the ability to think together.

That’s why this series keeps returning to the same hinge: the ability to ask for meaning without turning it into a fight.

Three repair moves you can use immediately

You don’t need to become a conversation analyst to practice repair. You need a small set of phrases you can trust when the room gets hot.

1) The “stance first” repair

Start by declaring your intent before you ask.

“I want to understand your model before I react. What do you mean by X here?”

This lowers threat and buys you clarity.

2) Candidate understanding with an escape hatch

This is the most practical move for real life.

“Let me check I’m tracking. When you say X, do you mean Y?”
“If that’s not it, what do you mean?”

The escape hatch matters. It signals: I’m not cornering you. I’m testing a hypothesis.

3) The “pinpoint the trouble type” move

Use the taxonomy you’ve already seen in this series. Choose one target and ask that question.

• Reference: “Who are we talking about, specifically?”

• Scope: “Which cases count, and which cases don’t?”

• Mechanism: “How does X lead to Y—step by step?”

• Implication: “Are you implying X, or am I reading that into it?”

Strong repair starts with diagnosis.

Why I’m using the elevator story again

Because it keeps me honest.

I can write about repair like it’s a public virtue. The elevator story shows repair as a private discipline. It shows the cost of skipping it. It shows how quickly a human mind fills in missing information with narrative, then treats that narrative as evidence.

It also shows the humility built into repair: the willingness to update your map while you’re still holding the wheel.

Repair asks for one thing: a pause long enough for reality to re-enter the conversation.

Repair is a pause with a purpose.

Carry it into your week

Pick one repair move and use it three times before Sunday.

Here’s the simplest one:

“Let me check I’m tracking. When you say X, do you mean Y?”

Use it once at home. Once at work. Once online.

Then notice what changes:

• Does the temperature drop?

• Does the other person clarify?

• Do you feel less pressure to “win”?

• Do you hear your own assumptions earlier?

If you want to share, drop your example in a Note. The series gets sharper when we build this from real interactions, not abstract ideals.

Next

Next comes the thing most readers actually want: scripts.

You already have the taxonomy, the diagnostic. The next tool post will be protocols: exact phrasing for work meetings, family friction, comment threads, and teaching. The point stays the same: a repair move that preserves dignity while restoring meaning.

Repair is the bridge. It takes meaning out of theory and puts it where we live: in the middle of the sentence, when your pulse has already started rising.

Shared reality doesn’t maintain itself. We maintain it—one repair at a time.

Skip the two foundational links below if you have already read them.

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The premise

A lot of American discourse right now is “argument” built on a missing step: we never agree on what a key word is doing. “Freedom.” “Safety.” “Woke.” “Accountability.” “Midnight.” We treat the word like a shared object, then we punish each other for not holding the same one.

This post is the first carry-it-into-your-week anchor.

It’s a taxonomy of the most common “meaning failures,” so you can ask for the right kind of clarity: definition, scope, mechanism, evidence, implication, values, and more.

In conversation analysis terms, this is repair (more to come on the repair topic later): how humans flag trouble and fix understanding mid-stream. Repair exists because problems of speaking/hearing/understanding are normal.

In cognitive science terms, it’s also grounding: updating common ground so the conversation can keep moving without having to guess.

A 60-second diagnostic

Before you ask anything, pick the failure point. Most confusion falls into one of these bins:

1. What are we talking about, exactly? → Reference

2. Which cases count? → Scope/boundaries

3. How does it work? → Mechanism

4. Is it true? → Evidence/standard

5. What’s being suggested without being said? → Implicature/presupposition

6. Why does it matter? → Values/stakes

7. What do you want done? → Action/next step

If you pick the bin, your question gets sharper and the temperature drops.

Tone key (so it lands as curiosity)

A clarification question can sound like a cross-examination if the other person is fused to the claim. So lead with one sentence that protects face and signals collaboration:

• “I want to understand your model before I react.”

• “Help me map what you mean, so I don’t straw-man you.”

• “I may be missing context, can you tighten this for me?”

Then ask your targeted question.

Politeness research treats these as ways of reducing the perceived threat of a request.

The taxonomy: 12 targets for “What do you mean?”

Use this like a menu. You don’t need all twelve. You need the right one.

1) Definition

Use when: a keyword is doing heavy lifting.
Ask: “When you say X, what do you mean by it in this context?”

Example: “When you say ‘accountability,’ do you mean consequences (punishment), transparency, or ownership?”

2) Reference (who/what exactly?)

Use when: the claim feels vague because it points at a fog.
Ask: “What specifically are you referring to: who, what event, what policy, what example?”

Example: “When you say ‘they,’ who do you mean: leadership, voters, media, agencies?”

3) Scope (boundaries + edge cases)

Use when: two people agree on the center and fight on the edges.
Ask: “Which cases count, and which cases don’t?”

Example: “When you say ‘free speech,’ are you talking about legal protection, platform rules, or social norms?”

4) Time (when + how long?)

Use when: disagreement is really about timeline.
Ask: “What time window are we talking about: today, this year, decades? What’s ‘before’ and ‘after’ here?”

Example: “When you say ‘recently,’ do you mean last week, last month, post-2020?”

5) Mechanism (how does X lead to Y?)

Use when: people are trading conclusions without a causal story.
Ask: “Walk me through the mechanism, how does X produce Y, step by step?”

Example: “How exactly does that policy change lead to that outcome? What’s the chain?”

(Mechanism questions are powerful because they force the claim to show its work.)

6) Evidence (what would count?)

Use when: the argument is stuck in “I feel” vs “prove it.”
Ask: “What evidence are you using, and what evidence would change your mind?”

Example: “What would you accept as a credible source here: data, firsthand reporting, audits, court records?”

7) Source/provenance (where did this come from?)

Use when: the claim arrives preloaded with certainty.
Ask: “Where did you first encounter this, and what makes that source reliable to you?”

Example: “Is this from a primary document, an article, a clip, a secondhand summary?”

8) Implicature (what’s being suggested?)

Use when: the conclusion is implied, not owned.
Ask: “Are you implying X, or am I reading that into it?”

This is classic Grice: people infer meaning to keep conversation coherent, and that inference is where conflict often ignites.

9) Presuppositions (what must be true for this to make sense?)

Use when: the framing smuggles in assumptions.
Ask: “What are we assuming as a given here? What would have to be true for your claim to hold?”

Example: “That question assumes intent; are we sure intent is established?”

10) Values/criteria (what are we optimizing for?)

Use when: facts are being used to serve a value conflict.
Ask: “What value is driving your position: fairness, safety, liberty, loyalty, harm reduction?”

Example: “Is your priority preventing harm, preserving autonomy, or enforcing consistency?”

11) Stakes/intent (what are you trying to accomplish?)

Use when: the conversation feels like it has hidden goals.
Ask: “What outcome are you aiming for by saying this: persuasion, warning, venting, coordination?”

Example: “Do you want me to agree, to act, or to understand where you’re coming from?”

12) Action/next step (what do we do now?)

Use when: meaning is clear enough, and the real gap is coordination.
Ask: “What would a good next step look like?”

Example: “What decision are we trying to make by the end of this conversation?”

Quick Scan (copy/paste)

If you only save one thing from this post, save this:

• “Define X in this context.”

• “What exactly are you referring to?”

• “Which cases count?”

• “What time window?”

• “What’s the mechanism?”

• “What evidence would change your mind?”

• “Where did this come from?”

• “Are you implying X?”

• “What assumptions are baked in?”

• “What value is driving this?”

• “What are you trying to accomplish?”

• “What’s the next step?”

Carry it into your week (starting today)

Pick one target from the taxonomy and use it three times before Sunday (if possible).

Here’s the rule: no rebuttal until you’ve run one targeted clarification question.

If you want a simple start, use Scope or Mechanism. They’re high-leverage and low-drama.

Careful promise: when the series is complete, I’ll compile the taxonomy + scripts into a single printable field guide for readers who want the whole kit in one place.

Next Up

Next: Repair—what conversation analysts learned from the way humans fix misunderstandings mid-stream. Repair is the bridge between “meaning in theory” and “meaning under pressure.”

Why “What do you mean?” is cognitive infrastructure hiding in plain sight

This isn’t the first time I’ve had this conversation, but in August 2007, inside a dimly lit tent during a military training exercise, a group of Marines and Sailors drifted from logistics into something heavier: obedience, moral certainty, and the old line, I’m just following orders.

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My Lai came up. The Holocaust came up. One Marine said, with total conviction, that he would never harm civilians. Another admitted he wasn’t so sure, boot camp and service conditions you in ways you don’t fully see.

It was turning into a values contest. Identity. Honor. Who’s the “good” Marine.

Then someone asked a simple question: “What do you mean by ‘conditioning’?”

And the room changed.

The debate stopped being a moral performance and became an inquiry into a mechanism, how training shapes behavior, how obedience gets built, how a human being can be guided into actions they would swear, in calmer conditions, they’d never take. One clarification question changed the level of analysis. We moved the conversation from posturing to reasoning.

That moment is this series in miniature.

Because here’s the thing most people miss: meaning isn’t an object that travels from one mind to another. Meaning is negotiated in real time. Without a repair move, people can use the same moral vocabulary while running completely different internal models.

So when I say this series is about “What do you mean?” I’m not talking about manners; rather, I’m talking about a tool that keeps humans from talking past each other, at work, at home, online, and in public life.

Part 1 is the foundation. To see why this question works, we need a firm grasp of three ideas: pragmatics, speech acts, and implicature.

Quick orientation on what you’ll walk away with from this series:

• A taxonomy of clarification targets (definition, scope, evidence, mechanism, implication, values, presuppositions, and more).

• A set of micro-protocols (short scripts) for meetings, classrooms, online chats, and high-stakes conversations.

• A repeatable habit for media literacy: slowing down meaning before the narrative takes over.

Each post ends with a field test, because this work is meant to be used.

1) Pragmatics: meaning in use

Most people were taught that language is basically a labeling system: words point at things, definitions settle disputes, and the dictionary is the referee.

Pragmatics comes in and says: watch what happens when people actually talk. Watch what the words do in context: who’s speaking, to whom, with what history, under what pressure, toward what goal. Pragmatics is the study of meaning as it’s used and inferred in real situations, not just encoded in sentences.

A phrase like “That’s interesting” can be sincere curiosity, a warning shot, or a polite exit ramp. A question like “Are you done?” can be a request for information, or a threat in business casual.

Pragmatics is why “What do you mean?” is a family of questions aimed at different failure points: definition, scope, reference, implication, evidence, mechanism, and more.

Later in this series, I’ll name those targets and give you a menu you can use in minutes.

2) Speech acts: words are actions

Speech act theory gives you a clean lens: every utterance has at least three layers.

• Locution: the words spoken

• Illocution: the act performed by saying them (requesting, warning, accusing, inviting, promising)

• Perlocution: the effect it has (calming, provoking, motivating, humiliating)

We’re attuned in everyday conversation not primarily to sentences as strings of words, but to the acts those sentences perform.

This is why so many arguments feel “irrational.” People are often fighting over illocution, not content.

Think about how many conflicts start here:

• “I’m just saying…” (The listener hears an accusation.)

• “Relax, it was a joke.” (The listener hears contempt.)

• “I’m asking a question.” (The listener hears cross-examination.)

Your nervous system is reacting to what you think the other person is doing to you with their words.

A sharp version of “What do you mean?” targets that layer directly:

“Are you asking me to fix this, or are you venting?”

The goal is conflict prevention and not pedantry.

In status-heavy settings, clarification can sound like a challenge: Are you unclear? Are you wrong? Are you being evasive? The craft is asking in a way that protects dignity while still demanding precision.

3) Implicature: the unsaid that runs the show

Now we get to the part that drives modern discourse insane: implicature, meaning that is implied rather than stated.

Grice’s core insight was simple: in conversation, we assume cooperation. We assume people are trying to be understood. So when a statement seems incomplete, we infer what “must” be meant. That inference is often where fights begin.

Example:

• Person A: “The budget is tight.”

• Person B hears: “So you want to cut the project.”

• Person A hears the pushback as: “You’re refusing reality.”

Implicature is where someone can signal without owning the claim. It’s also where media manipulation thrives: scope creep (“everyone knows…”), presuppositions hidden inside questions (“why are they lying?”), insinuations that travel faster than evidence.

A media-literate “What do you mean?” interrogates the implied payload:

“When you say everyone, who exactly?”

“What would have to be true for that claim to hold?”

“Are you implying X, or am I reading that into it?”

This is the difference between getting captured by a narrative and forcing a claim to show its work.

4) Why this matters in cognitive science

If pragmatics explains why misunderstanding happens, cognitive science explains why we’re so confident while misunderstanding each other.

Two biases matter here.

The illusion of transparency

We routinely overestimate how visible our intent is to others, how much our internal context “comes through.” Gilovich, Savitsky, and Medvec call this the illusion of transparency: we assume people can read our internal states far better than they can.

That’s why speakers feel misunderstood, and listeners feel blamed for “not getting it.”

The illusion of explanatory depth

We also overestimate how well we understand mechanisms. Rozenblit and Keil showed that people often feel they understand causally complex systems in much greater depth than they actually do—until they’re asked to explain how the thing works step by step. Confidence collapses on contact with mechanism.

This is where “What do you mean?” turns into an intellectual tool.

When you ask for the mechanism (How, specifically, does X lead to Y?), you’re clarifying and stress-testing certainty.

Fernbach and colleagues found that when people are asked to explain how political policies work, perceived understanding drops, and extreme attitudes often soften. Mechanism questions induce a kind of intellectual humility.

That matters for media literacy because modern platforms reward the opposite: hot takes, compressed claims, identity signaling, speed, and “I won.”

“What do you mean?” is a speed bump for the nervous system and a stress test for the claim.

5) Clarification is interaction design

A good clarification question is accurate, and it’s hearable. It reduces threat, signals goodwill, and then asks for precision.

Here are three micro-protocols I use (and teach) because they work across domains:

• Workplace precision (A/B menu): “I want to execute this cleanly. When you say X, do you mean A or B?”

• Heated debate (mechanism check): “Walk me through the mechanism. How, specifically, does X lead to Y?”

• Crucial conversation (fact vs. story): “I noticed [observable fact]. I’m telling myself a story that it means [interpretation]. Is that what you meant?”

Notice the pattern: you’re not starting with a verdict. You’re starting with a request for meaning that keeps both people in the conversation.

This is where this series becomes a toolkit.

Over the next few posts, you’ll get two things you can keep on your desk:

1. A taxonomy of clarification targets (definition, scope, mechanism, implication, evidence, values, presuppositions, and more).

2. A set of protocols, short scripts for meetings, classrooms, online heat, and high-stakes conversations.

Careful promise: when the series is complete, I’ll compile the taxonomy + protocols into a single, printable field guide for readers who want the whole kit in one place.

Each part will end with a field test. This is meant to be usable and, with personal effort, understood.

6) Preview: repair—the human system for fixing understanding mid-stream

So far we’ve been in architecture: how meaning gets built (and how it breaks) through action, implication, and context.

Next, we go one level deeper into repair.

Conversation analysts noticed something most of us ignore: everyday talk has a built-in “quality control” system. Humans constantly test, correct, and renegotiate understanding turn by turn. When something goes off, we don’t abandon conversation; we need to repair it.

Schegloff, Jefferson, and Sacks famously described repair as addressing problems of “speaking, hearing, and understanding,” and they documented a strong preference for self-correction—people typically try to fix their own talk once a problem is flagged.

“What do you mean?” is one of the cleanest forms of other-initiated repair: a signal that the problem is meaning and not how loud it was said.

Next week: repair—what conversation analysts learned from how humans fix understanding mid-stream. We’ll look at why repair is structured the way it is, why conversation prefers self-correction, and how those patterns give us a practical template for clarification that works in real life.

That’ll be my bridge for you to “carry it into your week” posts that will anchor the series: the taxonomy and the protocols.

Field test (5 minutes)

This week, replace one rebuttal with one clarification. Ask exactly what you’re uncertain about (definition, scope, evidence, mechanism, or implication) and watch what happens to the temperature of the conversation.

If you try it, tell me what changed.

References (starter list)

• Pragmatics (Stanford Encyclopedia of Philosophy).

• Speech Acts (Stanford Encyclopedia of Philosophy).

• Implicature (Stanford Encyclopedia of Philosophy).

• Schegloff, Jefferson & Sacks (1977), “The preference for self-correction in the organization of repair in conversation” (PDF).

• Gilovich, Savitsky & Medvec (1998), “The illusion of transparency…” (PubMed).

• Rozenblit & Keil (2002), “The misunderstood limits of folk science…” (PMC).

• Fernbach et al. (2013), “Political extremism is supported by an illusion of understanding” (PubMed).

In everyday talk, we often think we understand each other, but research shows miscommunication is rampant. We live in a high-velocity, high-context world that prizes brevity over clarity, and we suffer from the “illusion of transparency,” assuming our own intentions and definitions are clear to others. The result is a crisis of coherence, where people use the same words for radically different ideas.

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In this environment, the simple question “What do you mean?” becomes a powerful (if overlooked) repair tool. Rather than revealing ignorance, it’s a sophisticated intervention that halts misunderstanding and restores precision. As my research will show, in our current polarized certainty, “‘What do you mean?’ is our most powerful technology for rebuilding the public sphere”.

From Socrates’ search for clear definitions to modern conversation analysts, scholars show that asking for clarification is the primary mechanism for semantic repair, a check against biases like the Illusion of Explanatory Depth. In other words, it’s a piece of cognitive infrastructure: a low-cost question that aligns our private maps of meaning and keeps our conversations intelligible.

A Pragmatic Toolkit Across Disciplines

This series treats “What do you mean?” as a phrase and as a toolset grounded in multiple fields. Philosophy and linguistics teach us that meaning is slippery. Socrates used “What is…?” to test hidden assumptions; Wittgenstein reminded us that “the meaning of a word is its use” in context. Austin and Searle’s speech-act theory tells us every utterance has a force (warning, request, etc.), and “What do you mean?” often decodes that force.

Grice’s maxims show we constantly infer “implicatures” (the unsaid), and this question forces speakers to make their intended meaning explicit. Conversation analysts like Sacks and Schegloff identify “What do you mean?” as a classic other-initiated repair: it literally stops the line of talk until understanding is restored.

And Habermas’ theory of communicative action shows that every claim carries unspoken validity tests (truth, rightness, sincerity). Asking “What do you mean?” puts those on the table.

In cognitive science, we see why we need it: we habitually overestimate our understanding (“hallucinating depth”) and fear appearing ignorant. Education and conflict-resolution experts, in turn, note that practicing clear questions builds intellectual humility and reduces escalations.

• Philosophy & Theory: Socrates’s elenchus, Wittgenstein’s language games, Austin and Searle's speech acts, Habermas's discourse – each perspective shows why “What do you mean?” sharpens our concepts and aligns our conversational frameworks.

• Pragmatics & Conversational Repair: Grice’s Cooperative Principle tells us we assume cooperation; when that fails, we seek implicatures. “What do you mean?” makes the implicit explicit. Conversation-analytic research finds it to be a formal repair initiator – more than just “huh?”, it challenges the meaning itself.

• Cognitive Science: Biases like the Illusion of Explanatory Depth mean we think we understand complex ideas until forced to explain them. “What do you mean?” is a simple debiasing move: it punctures our false sense of clarity.

• Civic Discourse: Precision in meaning is civic infrastructure. In democratic deliberation, every argument carries truth and moral claims, and asking “What do you mean?” is how we test them. It’s a practice of listening before judging – a habit of intellectual humility that our polarized times desperately need.

Together, these lenses show that clarification is not soft or frivolous but rigorous. It’s about treating language as fragile machinery: if we don’t ask, we let it break down.

What This Series Offers

Over the coming posts, we will build a practical toolkit. You’ll get:

• A taxonomy of clarification targets: We’ll break “What do you mean?” into precise categories (e.g., definition of a term, scope of a claim, underlying values or evidence, the mechanism or how behind a statement, etc.). Each target will come with real-world examples, so you learn to aim your question accurately.

• Micro-protocols for everyday use: Exact phrasings to try in different settings – in meetings, classrooms, family discussions, or heated debates. (For example, protocols to clarify scope in a work project or separate intent from impact in a tense exchange.) These are ready-made question “scripts” you can adapt on the spot and improve over time.

• A practice in cognitive & civic literacy: Beyond one-off tips, this series encourages a habit: treating clarification as a default move. That means updating your own mental models without defensiveness. It’s a skill for both personal growth and public discourse, a way to ask “What do you mean?” as a normal, respectful step in any conversation, rather than a confrontation. Over time, this builds a culture where precision in language is expected and valued.

Series Outline

Each post will tackle a different aspect of this practice. Tentatively, we will cover:

• Meaning in use: Why clarification works (drawing on pragmatics and speech-act theory).

• Repair in conversation: How breakdowns happen (the mechanics of repair in talk).

• Barriers to asking: Status, face, and cognitive traps that make us stay silent.

• Taxonomy of clarification: The series’ core toolkit, laying out a structured taxonomy of question-types to ask when seeking clarity. It explains categories like definitions (e.g., “What exactly does X mean?”), scope/context (e.g., “Does this apply universally or only in specific cases?”), implications, mechanisms, values, etc., and why each matters (for example, definition questions break down jargon into simpler terms, and context questions narrow the scope to avoid assumptions).

• Protocols for practice: Practical wordings and scenarios for meetings, teaching, and conflicts.

• Scaling up: Clarification as civic practice (thinking beyond one-on-one talk).

• Field test: A prompt to try one clarification instead of a rebuttal this week.

By the end, you’ll have a clearer sense of when and how to ask “What do you mean?” plus a roadmap to revisit. Think of this as a reader’s contract: we’ll explore why it matters, share frameworks to apply it, and invite you to experiment.

Above all, the goal is simple: in any conversation, big or small, reach for understanding before assumption. The payoff is precision in our shared language – the bedrock of constructive dialogue.