name: Say's Law (Supply Creates Its Own Demand)

one_liner: The classical claim that aggregate production generates the income constituting aggregate demand, so a general glut cannot persist — a conditional resting on flexible market-clearing prices, no permanent hoarding, and a loanable-funds market that routes saving into investment.

origin_subdomain: macroeconomics

core_idea:
Say's Law is the classical-economics claim that aggregate production generates the income that constitutes aggregate demand for output, so sustained economy-wide overproduction relative to demand — a *general glut* — cannot occur: any excess supply in one market is necessarily matched by excess demand in another, and market prices clear the mismatch. The canonical formulation is "supply creates its own demand," associated with Jean-Baptiste Say's *Traité d'économie politique* (1803), though the precise phrasing is Mill's (1848) and the underlying circular-flow reasoning was articulated across classical economics by Ricardo, James Mill, and Say himself.

The mechanism is a circular-flow identity: each unit of output produced generates an equivalent amount of factor income (wages, profits, rents); that income is either consumed or saved; saving flows into investment through the loanable-funds market at an equilibrating interest rate; therefore aggregate expenditure on consumption plus investment equals aggregate output by identity, and any shortfall in consumption is matched by investment demand at the market-clearing rate. The law depends on three load-bearing assumptions: that the price system (including the interest rate) clears all markets flexibly and without delay; that no income is permanently hoarded rather than either spent or lent; and that the loanable-funds market reliably channels saving into investment without a gap. Under those assumptions, the macro problem reduces to the supply side — study production possibilities, factor markets, and relative prices; aggregate demand is not an independent constraint.

Keynes's *General Theory* (1936) targeted Say's Law as the central error of classical economics, arguing that all three assumptions routinely fail: prices are sticky, particularly downward; liquidity preference can trap saving in money hoards rather than routing it into investment; and the interest rate may be unable to clear the loanable-funds market at positive values. The Great Depression of the 1930s was the canonical failure case — idle factories, unemployed labour, and hoarded cash coexisted for years, demonstrating that production had not created its own demand. The debate between classical/supply-side and Keynesian/demand-side macroeconomics is, at its core, a debate over whether Say's Law holds empirically in the relevant range of economic conditions.

scope_of_application:
Say's Law lives within macroeconomics, as a regime-conditional behavioral claim turning on the circular-flow identity; it operates wherever the question is whether aggregate supply funds aggregate demand at full employment, and its reach is the traditions that dispute that hinge. The ecological-energy-flow and producer-consumer-queue analogues borrow only the circular-flow shape carried by `equilibrium`, not the law's monetary content.

- **Classical macroeconomics** — the home turf: full-employment equilibrium is assumed because excess supply in any market is balanced by excess demand elsewhere (Say, Mill, Ricardo), reducing the macro problem to the supply side.
- **Supply-side economics** — the modern restatement: production-boosting tax cuts are taken to be self-financing because production creates the income to buy the added output, the law's "supply creates its own demand" applied to fiscal policy.
- **Keynesian / heterodox macroeconomics** — the law's failure case is the foundational motivation for demand-side intervention; the *General Theory* targets all three assumptions, with the 1930s general glut (idle factories, unemployed labor, hoarded cash) as the diagnostic the identity failed to bind.
- **The supply-side-versus-demand-side debate** — structurally a single disagreement over whether Say's Law holds in the relevant range, so the law names the precise hinge on which the two traditions turn and the regime switch (supply-determined versus demand-constrained) between them.
- **Monetary and loanable-funds theory** — the law's dependence on a loanable-funds market clearing saving into investment at an equilibrating interest rate, and on the absence of liquidity-preference hoarding, locates it in the analysis of how saving returns (or fails to return) as investment.

structural_signature:
**Sig role-phrases:**

- **aggregate output** — the total production an economy generates, the supply side of the identity
- **the factor-income generation** — each unit of output generating an equivalent unit of factor income (wages, profits, rents)
- **the spend-or-lend disposition** — that income either consumed or saved, with saving routed into investment
- **the circular-flow identity** — aggregate expenditure (consumption plus investment) equalling aggregate output by construction, provided nothing leaks from the loop
- **the loanable-funds clearing** — saving channeled into investment through the loanable-funds market at an equilibrating interest rate
- **the binding-at-aggregate claim** — the behavioral assertion that the identity binds economy-wide at full employment, so a general glut cannot persist
- **the three load-bearing assumptions** — flexible market-clearing prices including the interest rate, no permanent hoarding outside the loop, and a loanable-funds market that reliably routes saving into investment
- **the leak / failure case** — a demand shortfall located as income leaving the circular flow (hoarding, or saving that fails to become investment), the 1930s coexistence of idle factories, unemployed labor, and hoarded cash as the diagnostic the assumptions broke
- **the supply-determined-versus-demand-constrained regime switch** — all three assumptions holding reduces the macro problem to the supply side; one failing makes demand the binding constraint
- **the fallacy-of-composition boundary** — the move from per-producer income-funds-spending (an identity) to aggregate supply-funds-demand is invalid unless the frictions are absent

what_it_is_not:
- **Not a law in the sense of an unconditional truth.** "Supply creates its own demand" is a *conditional* claim resting on three load-bearing assumptions — flexible market-clearing prices including the interest rate, no permanent hoarding outside the spend-or-lend loop, and a loanable-funds market that routes saving into investment. Keynes argued all three routinely fail; the 1930s coexistence of idle factories, unemployed labor, and hoarded cash is the canonical demonstration that production had *not* created its own demand.
- **Not the per-producer truism that income funds spending.** At the level of a single producer "my income is the proceeds of my output" is an identity that always holds. The law's actual, contested content is the *aggregate* claim that economy-wide supply funds economy-wide demand — and the slide from the individual identity to the aggregate is a *fallacy of composition* unless the frictions are absent.
- **Not the circular-flow identity itself.** The accounting identity (output generates equivalent income, consumed or saved-and-invested) is true by construction. Say's Law is the *behavioral* addition — that the identity *binds at full employment*, with nothing leaking from the loop — which is exactly the part that can fail when saving is hoarded or fails to become investment.
- **Not the marketing maxim "build it and they will come."** It is not the micro claim that a good product generates its own buyers, or that any individual output finds demand. It is a macro claim about *aggregate* income generation across all markets — a single firm's output can certainly go unsold; the law concerns whether a *general glut* across the whole economy can persist.
- **Not the general equilibrium or circular-flow pattern.** The substrate-spanning structures it packages — the circular-flow identity and generic market-clearing `equilibrium` — do travel (ecological energy flow, producer-consumer queues share the balance *shape*). The binding-at-full-employment behavioral claim that makes this *Say's Law*, with its dependence on prices, interest rates, and money, has no analogue off-substrate; invoking it elsewhere is metaphor via the parents.

clarity:
Naming Say's Law isolates a proposition that is locally obvious yet globally contested, and keeping those two scales apart is its central clarifying service. At the level of a single producer the claim is almost a truism — the baker's income *is* the proceeds of selling bread, so her capacity to demand shoes is funded by her supply of loaves. The law's content is the assertion that this scales to the aggregate: that economy-wide, supply in total funds demand in total, so a *general glut* is impossible. By giving the aggregate claim a name distinct from the per-producer intuition, the concept lets a macroeconomist see that the interesting question is never whether individual income funds individual spending (it does, by identity) but whether the identity *binds* across the whole economy without monetary, expectational, and coordination frictions intervening.

That reframing converts the sprawling dispute between classical/supply-side and Keynesian/demand-side macroeconomics into a single decidable question: *do the three load-bearing assumptions hold in the relevant range?* — flexible market-clearing prices including the interest rate, no permanent hoarding of income outside the spend-or-lend loop, and a loanable-funds market that reliably routes saving into investment. Naming the law this way makes legible that "supply creates its own demand" is not a free-standing truth but a *conditional* one whose validity rides entirely on those premises, so the practitioner can stop arguing about the slogan and start asking which assumption fails in a given episode. It sharpens, in particular, the question of whether aggregate demand is an independent constraint on output at all: if Say's Law holds, the macro problem reduces to the supply side and demand needs no separate study; if it fails — sticky prices, liquidity preference trapping saving in money, an interest rate that cannot clear loanable funds at positive values — then demand becomes the binding constraint, and the 1930s coexistence of idle factories, unemployed labor, and hoarded cash is exactly the diagnostic that the assumptions broke. The law thus gives a name to the precise hinge on which the supply-side and demand-side traditions turn.

manages_complexity:
Say's Law's compressive payoff, in the classical framework where it holds, is to fold an entire dimension of the macroeconomic problem out of view. The economy is, in full, a tangle of countless interacting markets — every good, every factor, the loanable-funds market — each capable of its own surplus or shortfall, and aggregate demand could in principle be an independent quantity that must be tracked alongside aggregate supply, modeled, and managed. If supply creates its own demand, that second quantity is not free: it is pinned to production by the circular-flow identity, so a general glut cannot persist and aggregate demand cannot bind as a separate constraint. The macroeconomist studying a classical economy therefore drops demand-side analysis entirely and tracks only the supply side — production possibilities, factor markets, relative prices — reading aggregate demand off output rather than determining it independently. A two-sided problem collapses to a one-sided one.

But the law's deeper organizing service is to compress the sprawling, century-long quarrel between the supply-side and demand-side traditions into a single small checklist. Rather than re-arguing the slogan episode by episode, the analyst recognizes "supply creates its own demand" as a *conditional* resting on exactly three load-bearing assumptions — flexible market-clearing prices including the interest rate, no permanent hoarding of income outside the spend-or-lend loop, and a loanable-funds market that reliably routes saving into investment — and reads any macroeconomic regime by asking which of the three holds. All three hold: the classical world, where the macro problem reduces to supply. One or more fail: demand becomes the binding constraint and the Keynesian apparatus applies — sticky prices, liquidity preference trapping saving in money, an interest rate that cannot clear loanable funds, with the 1930s coexistence of idle factories, unemployed labor, and hoarded cash as the diagnostic that the assumptions broke. The qualitative regime — supply-determined or demand-constrained — is read off three binary conditions rather than re-derived from the full multi-market system each time.

abstract_reasoning:
Say's Law licenses a characteristic set of moves in macroeconomic analysis, all turning on the circular-flow identity and the three load-bearing assumptions that decide whether it binds at the aggregate.

*Diagnostic (read a demand-side regime by testing the three assumptions).* The central move is to determine, for a given economy or episode, whether supply does in fact create its own demand — and the concept reduces that to checking three conditions: do prices (including the interest rate) clear markets flexibly; is income spent or lent rather than permanently hoarded; does the loanable-funds market reliably route saving into investment? The signature diagnostic runs from an observed pattern of *coexisting idle resources* — unsold output, unemployed labor, and hoarded cash standing together — to the inference that the identity has *failed to bind*, because under Say's Law that combination cannot persist (the income from production would have funded the demand to clear it). The 1930s coexistence of idle factories, unemployed workers, and money hoards is read precisely as the fingerprint that one or more assumptions broke: sticky prices, liquidity preference trapping saving in money, or an interest rate unable to clear loanable funds. The reasoning thus moves from a surface signature (persistent general glut) to a hidden cause (which premise failed), with the three assumptions as the closed list of candidates.

*Boundary-drawing (separate the per-producer truism from the aggregate claim, and locate the regime).* The most important inferential discipline the law imposes is a scale boundary: at the level of a single producer "my income is the proceeds of my output" is an identity and always holds, so the move "individual income funds individual spending, therefore aggregate supply funds aggregate demand" is flagged as the characteristic fallacy of composition unless the three frictions are absent. The concept rules out treating the slogan as an unconditional truth and insists it is a *conditional* whose validity rides on its premises. This boundary also partitions the macroeconomic regime: where all three assumptions hold, aggregate demand is ruled *not* to be an independent constraint and the analysis is confined to the supply side; where one fails, demand becomes the binding constraint and the supply-side reduction is out of bounds. Deciding which side of that line an economy sits on is the load-bearing judgment, and it is what the supply-side and demand-side traditions actually disagree about.

*Interventionist (the lever depends on which regime holds).* The law makes policy prescription regime-contingent and predicts that the wrong-regime intervention misfires. If Say's Law holds, output is supply-determined, so the predicted lever is the supply side — production possibilities, factor markets, relative prices — and demand-management is predicted to be inert because demand is already pinned to output by identity. If the law fails, the predicted lever shifts to demand: the binding problem is that saving is not returning as investment at the prevailing interest rate, so closing the demand gap (rather than expanding productive capacity) is what moves output. The interventionist content is therefore a conditional fork — the same policy is predicted to work or fail depending on which assumptions hold — and the concept's job is to tell the practitioner which fork they are on before prescribing.

*Identity / accounting reasoning.* Beneath the regime question, the law licenses reasoning from the circular-flow identity itself: each unit of output generates an equivalent unit of factor income, which is either consumed or saved-and-invested, so aggregate expenditure equals aggregate output *by construction* — provided nothing leaks out of the spend-or-lend loop. The analyst can therefore reason that any apparent aggregate demand shortfall must correspond to a *leak* (hoarding, or saving that fails to become investment), and locate the failure at that leak rather than in production. This converts "where did demand go?" into the bookkeeping question "where did income leave the circular flow?", with the answer constrained to the points where the identity's no-leakage assumption can break.

knowledge_transfer:
Within macroeconomics Say's Law transfers *as mechanism* — as a load-bearing interpretive claim with a definite truth-value in a given regime — across the traditions that turn on it. The same circular-flow reasoning and the same three load-bearing assumptions (flexible market-clearing prices including the interest rate, no permanent hoarding outside the spend-or-lend loop, a loanable-funds market that routes saving into investment) organize classical macroeconomics (full-employment equilibrium because excess supply in any market is balanced elsewhere), supply-side economics (the modern restatement that production-boosting tax cuts are self-financing because production creates the income to buy the added output), and the Keynesian/heterodox critique (the law's failure case as the foundational motivation for demand-side intervention). The diagnostics carry with the vocabulary — read a general glut as the identity failing to bind; separate the per-producer truism from the aggregate claim; locate any demand shortfall as a *leak* from the circular flow (hoarding, or saving that fails to become investment); and make policy regime-contingent (supply-side levers if the law holds, demand-side if it fails). The whole supply-side-versus-demand-side debate is, structurally, a disagreement over whether Say's Law holds in the relevant range, so the concept's reach across those camps is genuine mechanism, not loose analogy — they share the identity and dispute its premises.

Beyond macroeconomics the honest reading is **shared-abstract-mechanism** (B) shading into **metaphor** (A), and the named claim itself does not travel. What genuinely recurs across substrates is not "supply creates its own demand" but the more general structures the law packages: the *circular-flow identity* (a candidate `circular_flow_of_income` prime) and, above it, generic `equilibrium` and market-clearing. Those general patterns are the things that travel, and the cross-domain lesson should be carried by them. The cited analogues — energy flow in ecology, producer-consumer queues in software, reciprocal exchange in social systems — do share a circular-flow or balance *shape*, but they pick up that shape from the `equilibrium` / circular-flow parents, not from Say's Law; invoking "supply creates its own demand" for them is metaphor and should be marked as such, because none carries the law's specific behavioral content.

The home-bound cargo is exactly what makes Say's Law *this* contested proposition rather than the bare identity: the assertion that the circular-flow identity *binds at the aggregate, at full employment*, under Walrasian price flexibility, the absence of liquidity preference, and Wicksellian loanable-funds equilibrium. That behavioral claim is substrate-specific — its truth-value depends on facts about prices, interest rates, and money in a monetary production economy — and it has no analogue in an ecosystem or a message queue, where there is no interest rate to clear saving against and no liquidity preference to trap it. So the identity-level shape travels to the parents while the binding-at-full-employment claim stays home. One discipline travels usefully wherever the circular-flow shape is borrowed and is the law's sharpest lesson: the move from "individual income funds individual spending" to "aggregate supply funds aggregate demand" is a *fallacy of composition* unless specific frictions are absent — so any cross-domain "the parts balance, therefore the whole balances" inference inherits exactly that caution, and must check whether something leaks from the loop at the aggregate. Mechanism within macroeconomics (a regime-conditional behavioral claim), circular-flow / `equilibrium` recurrence plus metaphor beyond — the profile *Structural Core vs. Domain Accent* makes precise.


Scale-Before-Fit

one_liner: Diagnose a venture's failure as one of ordering — committing substantial growth investment before demonstrating repeatable, unsubsidised demand — by asking whether the evidence at the moment of commitment justified the cost base it locked in.

status: draft

core_idea: Scale-before-fit is the venture-strategy anti-pattern in which an organisation commits substantial growth investment — sales-force expansion, manufacturing capacity, geographic rollout, hiring ahead of revenue, paid-acquisition spend — before it has demonstrated repeatable, unsubsidised demand and a repeatable operating model at a smaller scale, with the result that what gets scaled is either an unprofitable per-unit operation, an operating model that breaks at the larger scale, or a product whose demand profile does not support the cost base the expansion created. The structural sequence of the failure is: capital and headcount commitments lock in a cost base at a scale the demand and operating evidence cannot support; the operation runs at a structural loss that is misread as normal growth-stage investment; and the organisation burns down its reserves trying to reach the level of demand and operating refinement that would justify the expansion, or contracts painfully back to a sustainable scale. The distinction the concept draws is temporal and not about technical quality: the failure is not in the scaling operation itself and not in the underlying offering's potential, but in the ordering — committing to growth investment before the evidence that the underlying offering and operating model are repeatable. The lean-startup framing names this "premature scaling"; the Startup Genome Report identified it as a leading cause of early-stage startup failure; the same pattern appears in corporate innovation programmes that move a successful proof-of-concept to enterprise-wide rollout before validating that the pilot's operating conditions hold at scale, and in international expansions that replicate a home-market model into geographies whose regulatory environment, supply chain, or customer preferences break the model.

scope_of_application:
Scale-before-fit lives within venture and innovation strategy; its reach is within that domain, across the whole class of cohort-based-rollout ventures, wherever growth investment can be committed before repeatable, unsubsidised demand and a repeatable operating model are demonstrated. The varied "domains" are one cohort-rollout substrate replayed — for genuinely distant scale-ups (engineering, ecological introductions, infrastructure build-out) carry the broader pilot-to-scale / delayed-feedback / commitment-lock-in parents, not "scale-before-fit" by name.

- **Early-stage startups** — the sharpest home: the lean-startup "premature scaling" framing and the Startup Genome Report's finding that it is a leading cause of early-stage failure.
- **Corporate innovation programmes** — moving a successful proof-of-concept to enterprise-wide rollout before validating that the pilot's operating conditions hold at scale.
- **Public-sector innovation** — scaling a demonstration pilot into national rollout before validating that the demonstration's local operating conditions replicate.
- **Education reform** — charter networks and curricular interventions scaling from a selected-population pilot before validating that a particular leadership-profile or context dependence transfers.
- **Health-tech adoption** — signing payor contracts nationally before validating reimbursement, clinical-workflow, and patient-engagement fit beyond the home market.
- **International expansion** — replicating a home-market model into geographies whose regulation, supply chain, or customer preferences break it.

structural_signature:
**Sig role-phrases:**

- **the repeatability evidence** — the demonstration owed before scaling: unsubsidised recurring demand, unit economics that hold, an operating model shown to survive the next volume step
- **the scale commitment** — the substantial growth investment (sales-force expansion, manufacturing capacity, geographic rollout, hiring ahead of revenue, paid-acquisition spend) being made
- **the locked-in cost base** — the fixed cost structure the commitment creates at a scale the evidence cannot yet support
- **the out-of-order commitment** — the load-bearing defect: the growth investment is committed *before* the repeatability evidence exists, a failure of sequence, not of the offering or the rollout
- **the structural loss masquerading as growth burn** — the operation runs at a per-unit loss that is misread as normal growth-stage investment rather than the cost of having over-committed
- **the delayed surfacing** — the gap stays invisible at commitment and materialises only over later cohorts and quarters (rising blended CAC, worse-retaining cohorts, the model straining at volume), by which point reserves are committed
- **the burn-or-contract endgame** — reserves drawn down chasing demand that would justify the cost base, or painful contraction back to the scale the evidence supports
- **the evidence-gate corrective** — condition each scaling commitment on specific repeatability evidence and withhold capacity/headcount/spend until it exists; once the gate is failed, contraction (not "more time" or "more spend") re-matches cost base to validated demand

what_it_is_not:
- **Not a botched scaling operation.** The rollout can be executed flawlessly and the venture still fails, because the defect is in the *ordering* — committing growth investment before the evidence that demand and the operating model are repeatable — not in how the scaling was run. Diagnosing it as poor execution points the correction at the rollout machinery when the broken thing is the decision sequence.
- **Not a weak product.** The underlying offering can be sound and have real potential; scale-before-fit is agnostic about technical quality. Post-mortems routinely fuse three separable questions — was the product viable, was the rollout executed well, was scaling attempted too early — and this anti-pattern is specifically the third. The fingerprint is each cohort's economics worsening as the venture grew, not an offering that never worked.
- **Not growth-stage burn.** "We're losing money but that's just growth investment" is not self-certifying here: the test is whether the demand-and-operating evidence available *at the moment of the scale commitment* justified the cost base it locked in. If it did not, the loss is structural — the cost of having committed to a scale the evidence could not support — and "more time" or "more spend" cannot retroactively make an un-validated model repeatable.
- **Not the failure of never scaling.** It is the sign-reversed opposite of pilot purgatory: applying the aggressive scale *before* fit is this anti-pattern, while applying the cautious evidence-gate *after* fit — refusing to scale a validated model — is its own distinct failure. Once repeatable, unsubsidised demand is demonstrated, aggressive scaling is correct, even obligatory; the error is timing relative to fit, in either direction.
- **Not a failure of direction or nerve.** Scale-before-fit is a timing failure of a *growth commitment*, distinct from changing direction too often (a direction failure) and from being too timid to grow (a nerve failure). Before invoking it, the diagnosis must confirm the problem is *when* a scale commitment was made — not what the venture is pursuing or whether it dared to act.

clarity:
Naming scale-before-fit isolates the failure as one of *ordering*, which the broader complaint "the scaling went wrong" leaves buried. When growth investment outruns demand and the reserves burn down, the reflexive diagnoses point at the scaling operation (the rollout was botched) or at the product (the offering was weak). The label makes legible a third possibility that is usually the real one: both the scaling and the offering can be sound, and the venture still fails, because the growth commitment was made *before* the evidence that the demand and operating model are repeatable. That separates three things venture post-mortems routinely fuse — *was the product viable?*, *was the scaling executed well?*, and *was scaling attempted too early?* — and points the correction at the decision sequence rather than at execution or at the offering.

It also sharpens what counts as a structural loss versus growth investment, a distinction that lets a doomed expansion masquerade as healthy burn. Once the anti-pattern is named, "we are losing money but that is just growth-stage spend" is no longer self-certifying: the sharper question becomes whether the demand and operating evidence *available at the moment of the scale commitment* justified the cost base that commitment locked in. If it did not, the loss is not investment toward a known model — it is the cost of having committed to a scale the evidence could not support. That reframing is what tells the operator the remedy is not "more time" or "more spend" but *evidence-gating the next scaling decision* — and it explains why the same correction (validate repeatability at small scale first) applies whether the unit being scaled is a sales force, a charter-school model, or a pilot programme headed for national rollout, since in each the broken thing is the order of commitment, not the thing committed to.

manages_complexity: A venture's growth crisis arrives as a tangle — rising acquisition costs, worsening cohort retention, an operating model straining at the new volume, a board asking whether to spend more or pull back, a runway clock running down — and each failed expansion reads as its own story with its own numbers. Scale-before-fit compresses that tangle into a five-slot schema: the demand-and-operating evidence available at the moment of the scale commitment, the scale commitment itself, the cost base that commitment locked in, the gap between that cost base and the validated revenue per unit, and the burn horizon over which the gap must close. The whole diagnosis then reduces to a single question read off those slots — did the evidence at decision time justify the cost base the commitment created? — so the operator classifies the situation as scale-before-fit or not without re-arguing the product's merit and the rollout's execution separately. The downstream behaviour (structural loss masquerading as growth burn, reserves drawn down, contraction back to a sustainable scale) follows from the same few quantities, and the remedy points at evidence-gating the next commitment rather than at more time or more spend. An open-ended "why is this expansion failing" becomes a short ledger whose evidence-versus-cost-base term decides the case.

abstract_reasoning:
Scale-before-fit licenses inference moves that all hinge on the temporal comparison the compression isolates: the evidence available *at the moment of the scale commitment* against the cost base that commitment locked in.

*Diagnostic — distinguish a structural loss from growth investment by back-dating the evidence.* The characteristic move is to take a venture that is losing money during expansion and decide whether the loss is healthy growth burn or the cost of having scaled too early — and the discriminating question is not the present numbers but a counterfactual about the past: *did the demand-and-operating evidence that existed when the scale commitment was made justify the cost base it created?* If yes, the loss is investment toward a known model and "we just need more time" is legitimate; if no, the loss is structural and time will not close it. The diagnostic signature that the gate was skipped is a cluster that appears *after* the commitment: blended acquisition cost rising as easy channels exhaust, later cohorts retaining worse than the early ones, and the operating model straining at the new volume in ways it did not at the pilot. From "burn is high and rising and the new cohorts look worse than the old," the move is to infer that what was scaled was not yet repeatable — that the early evidence was favorable because of selection (the first cohorts were the most product-fit) rather than because of a repeatable model. A second diagnostic separates three things post-mortems fuse: a weak product, a botched rollout, and a sound product scaled too early — and reads "each cohort's economics worsened as we grew" as the fingerprint of the third, not the first two.

*Interventionist — gate the next commitment on evidence; do not buy time or spend harder.* Because the defect is ordering, the licensed intervention acts on the *decision sequence*, not on execution or the offering. The move is to condition each scaling commitment on specific repeatability evidence — unsubsidised demand that recurs, unit economics that hold at the validated scale, an operating model demonstrated to survive the next volume step — and to *withhold* the capacity, headcount, or acquisition spend until that evidence exists. Each gate is a prediction: hold the commitment until repeatability is shown and the cost base will be sized to demand the evidence supports; release it on capital availability, competitive pressure, or sponsor enthusiasm instead, and the prediction is a structural loss misread as growth burn followed by reserve depletion. When the gate has already been failed, the interventionist reading predicts that "more time" and "more spend" will fail (they cannot retroactively make an un-validated model repeatable) and that the recovering move is *contraction back to the scale the evidence supports* — painful, but the only one that re-matches cost base to validated demand.

*Boundary-drawing — which regime is the venture in, and is this even scale-before-fit?* The concept draws two lines. First, it separates the pre-fit regime (where the binding question is *is the model repeatable?* and the correct posture is to validate at small scale before committing) from the post-fit regime (where repeatability is shown and aggressive scaling is correct, even obligatory against competitors). Applying the cautious evidence-gate after fit is its own failure (pilot purgatory — never scaling a validated model); applying the aggressive scale before fit is this anti-pattern. The move is to locate the venture on that line by asking whether unsubsidised, repeatable demand has actually been demonstrated, not assumed. Second, it bounds the diagnosis against orthogonal failures: scale-before-fit is *too-early scaling*, distinct from pivoting too often (a direction failure) and from never scaling (a timidity failure) — so the move is to check that the failure is one of *timing of a growth commitment*, not of direction or nerve, before invoking it.

*Order-of-events — the sequence is the prediction, and the cost surfaces on a delay.* The concept asserts a fixed ordering (validate repeatable demand and operating model → then commit growth investment → then scale) and predicts that violating it produces a *delayed* surfacing of the unit-economics gap: the commitment locks in the cost base immediately, but the evidence that the demand could not support it arrives only over the cohorts and quarters that follow, by which point reserves are committed. The move reads timing as confirmation — a loss that was invisible at commitment and materialised only as later cohorts came in worse is the order-of-events fingerprint of scaling ahead of fit — and reads it forward as a forecast: a venture committing capacity now on pilot-only evidence is predicted to discover the gap late, when contraction is the only remaining lever.

knowledge_transfer:
Within venture and innovation strategy the diagnosis transfers *as mechanism* across the whole class of cohort-based-rollout ventures, because the five-slot ledger it isolates — evidence available at the scale commitment, the commitment, the cost base it locked in, the gap to validated revenue per unit, the burn horizon — is the same regardless of what is being scaled. The back-dated evidence test (did the evidence at decision time justify the cost base?), the diagnostic signature of a skipped gate (blended acquisition cost rising as channels exhaust, later cohorts retaining worse than early ones, the operating model straining at the new volume), the evidence-gating intervention, and the contraction-back-to-supported-scale remedy carry intact from **early-stage startups** (the lean-startup "premature scaling" framing; the Startup Genome Report's leading-cause-of-failure finding), to **corporate innovation** programmes moving a proof-of-concept to enterprise-wide rollout before validating that the pilot's operating conditions hold, to **public-sector innovation** (a demonstration pilot scaled to national rollout), to **education reform** (a charter network scaling from a selected-population pilot before validating that its leadership-profile reliance transfers), to **health-tech** (signing payor contracts nationally before validating reimbursement and clinical-workflow fit), to **international expansion** (replicating a home-market model into geographies whose regulation, supply chain, or customer preferences break it). These are not analogies between separate problems; they are flavours of *one* substrate — cohort-based intervention scaling — with the unit being scaled (a sales force, a charter model, a pilot programme) swapped, so the cohort-economics and operating-model-fit audits transfer between them essentially unchanged.

The reach of the *named* concept stops at the edge of that substrate, and honesty requires marking why its apparent breadth is one substrate replayed rather than cross-domain travel. Strip the venture-strategy framing — product-market fit, lean-startup vocabulary, stage-gates, runway, CAC-payback discipline, the operating-model maturity model — and that framing, load-bearing in venture work, is exactly the home-bound cargo. The varied "domains" above transfer because they share the cohort-rollout substrate, not because each independently re-instantiates a substrate-independent prime.

What genuinely travels to distinct substrates is the *pattern underneath* scale-before-fit, and that — not the named anti-pattern — is what should carry any cross-domain lesson (case B). Stripped of venture vocabulary the concept decomposes cleanly into substrate-general parents that recur everywhere: `pilot_to_scale_transition` (the broader pattern of pilot evidence underestimating scaled outcomes), `sunk_cost` / commitment-escalation (the lock-in a committed expansion creates), `feedback_loop_with_delay` (the unit-economics gap surfacing only over the cohorts and quarters after the commitment), `local_optimum` selection bias (early cohorts looking favourable because they were the most fit, not because the model was repeatable), and ordering/sequencing (the temporal commitment that is the whole defect). Those parents are genuinely substrate-independent — the "validate repeatability before committing the cost base, because committed capacity surfaces its gap on a delay" logic recurs in engineering scale-up, ecological introductions, clinical-trial-to-population rollout, and infrastructure build-out — and the cross-domain insight belongs to *them*. The honest report is therefore: across venture and innovation strategy's cohort-rollout flavours the diagnosis transfers as mechanism with only vocabulary changed; for genuinely distant domains, carry the general *pilot-to-scale / delayed-feedback / commitment-lock-in / selection-bias / sequencing* parents, while the venture-strategy apparatus and the "scale-before-fit" name stay home as the domain accent. (See *Structural Core vs. Domain Accent*.)


Schema Mapping Relation

core_idea:
A schema mapping relation is an explicit, typed assertion that a concept defined in one independently governed classification scheme corresponds, in a stated way, to a concept defined in a different scheme. The minimum commitment is five-part: a source scheme S1; a source concept c1 within S1; a target scheme S2; a target concept c2 within S2; and a *correspondence type* that grades the fidelity of the bridge — in the SKOS vocabulary from which the candidate originates, one of `exactMatch` (safe to substitute without loss), `closeMatch` (navigable but loss-risk should be declared), `broadMatch` (c1 is narrower than c2, the bridge climbs the hierarchy), `narrowMatch` (c1 is broader than c2, the bridge descends), or `relatedMatch` (associative link useful for browsing, not for inference). The relation is *explicit* rather than inferred, *cross-scheme* rather than internal, and *typed* so that downstream consumers know which fidelity guarantees the bridge actually makes.

The structural work the relation does is to bridge sovereign vocabularies without merging them: S1 and S2 keep their own governance, internal coherence, and update cycles, while the declared mapping records serve as versioned, revocable connective tissue through which a query against one scheme can be routed into the other. The fidelity grade is load-bearing: it converts the binary question "does a bridge exist?" into the actionable question "is this bridge's declared precision sufficient for this use?" — separating *correspondence* (a link exists at all) from *interchangeability* (substitution is safe here) from *equivalence* (the bridge is loss-free for all purposes). A mapping table is itself a governed artifact with its own changelog, distinct from the governance of either scheme it connects; gaps in the mapping — source concepts with no available target — become visible as first-class curatorial signals rather than silent failures. Composition rules also apply: if c1 exactly matches c2, and c2 closely matches c3, then c1 closely matches c3 by the weakest-link principle, allowing analysts to reason about fidelity loss propagating across mapping hops.

scope_of_application:
The schema mapping relation lives across the knowledge-organization and interoperability subfields of information science; its reach is bounded by one precondition — two separately-governed vocabularies that must be jointly queryable — and the loose analogues (two teams reconciling jargon) belong to the parent `function_mapping`, not here.

- **Library and museum knowledge-organization systems** — the home turf: SKOS-graded bridges between thesauri (LCSH ↔ FAST), classification schemes (DDC ↔ UDC), and structured vocabularies (CIDOC CRM ↔ local catalogue terms), where a curator reads `broadMatch` off a local-to-LCSH bridge and knows inference may climb but not descend.
- **Healthcare terminology crosswalks** — ICD-9 ↔ ICD-10, ICD ↔ SNOMED-CT, LOINC ↔ local lab codes, where the `closeMatch`-flags-loss-risk discipline is load-bearing because clinical and reimbursement consequences ride on the bridge's declared precision.
- **Bioinformatics ontology alignment** — Gene Ontology ↔ Disease Ontology, ChEBI ↔ DrugBank, and cross-species orthology mappings, where the graded-bridge-between-sovereign-catalogues structure recurs term-for-term.
- **Data engineering** — ETL field mappings, database-schema bridges during migration, and type-system bridges (`BigDecimal` ↔ JSON `number`), where a record is routed through the declared correspondence and fidelity is propagated arithmetically across hops.
- **Regulatory equivalency** — FDA ↔ EMA drug-approval and SEC ↔ ESMA disclosure-regime mutual-recognition declarations, each asserting that one standard maps to another at a stated fidelity for limited purposes.

structural_signature:
**Sig role-phrases:**

- **the source scheme** — a separately-governed classification vocabulary containing the concept mapped *from*
- **the source concept** — the element within the source scheme that the bridge originates at
- **the target scheme** — a distinct, separately-governed vocabulary containing the concept mapped *to*
- **the target concept** — the element within the target scheme the bridge lands on
- **the correspondence type** — a fidelity grade on the bridge (exact / close / broader / narrower / related) that is part of the assertion, not guessed by the consumer
- **the declaration record** — the explicit, versioned, revocable mapping artifact, governed independently of either scheme
- **the directional inference rule** — what each grade licenses and forbids (substitute blindly / flag loss / climb only / descend only / browse but never infer)
- **the weakest-link composition** — fidelity propagated across mapping hops, the lowest grade in a chain dominating the route's guarantee
- **the gap inventory** — source concepts with no available target, surfaced as a first-class curatorial signal rather than a silent miss

what_it_is_not:
- **Not a merge of the two schemes.** A mapping does not fold S1 and S2 into one master vocabulary; both keep their own governance, hierarchy, and update cycle, and the bridge is external, versioned, revocable connective tissue. The point is to make them jointly queryable *without* either renouncing its identity — the opposite of electing a single re-filing vocabulary.
- **Not an assertion of equivalence.** Filing a bridge does not claim the two concepts are interchangeable; only `exactMatch` claims that. `closeMatch`, `broadMatch`, `narrowMatch`, and `relatedMatch` each declare a weaker, often directional or browse-only correspondence — so a mapping records *that a link exists and how much fidelity it guarantees*, not that substitution is safe.
- **Not bijective.** The relation is typed and graded, and `broadMatch` / `narrowMatch` are explicitly one-to-many in one direction; a source concept may map to several targets or to none. Expecting one-to-one correspondence misreads a graded crosswalk as a clean isomorphism.
- **Not an inference at the moment of assertion.** Unlike analogy, which generates a claim by recognizing shared relational structure, a schema mapping is an *explicitly declared* bridge between named elements — no new proposition is derived when it is filed. Inference enters only later, downstream, and only within the directional limits the grade permits.
- **Not a silent lookup that fails quietly.** A source concept with no available target is not a missing-row accident but a first-class curatorial signal: the *gap* is a recorded fact about where two sovereign vocabularies fail to meet, countable and curatable, not an invisible miss.
- **Not a static, ungoverned table.** The mapping set is itself a governed artifact with its own changelog and life cycle (assert, review, deprecate, replace), distinct from the governance of either scheme. A `closeMatch` can be upgraded to `exactMatch` when one scheme catches up; the bridges are maintained, not frozen.

clarity:
Naming a cross-scheme link as a *typed, graded mapping* makes legible a distinction that interoperability practice routinely collapses: that a bridge *existing* and a bridge being *safe to substitute across* are different facts, and that a consumer needs to know which one a given assertion claims. Without the correspondence type, every crosswalk row reads as undifferentiated equality, and a routing layer treats `closeMatch` and `exactMatch` alike — silently importing loss wherever a near-match was filed as an equivalence. The fidelity grade dissolves that by forcing the three things apart at declaration time: *correspondence* (a link exists), *interchangeability* (substitution is safe in this context), and *equivalence* (the bridge is loss-free for every purpose). A curator can then ask the operative question of any bridge — *what fidelity does this claim, and is it sufficient for my use?* — rather than guessing a guarantee the assertion never made.

The relation also sharpens what it means for two schemes to relate at all. Because the mapping is explicit and cross-scheme rather than inferred and internal, the *gap* — a source concept with no available target — becomes a first-class fact about the relationship between S1 and S2, a curatorial signal rather than a silent lookup miss. And because each grade carries a direction and a guarantee (`broadMatch` climbs the hierarchy and forbids descending inference; `relatedMatch` supports browsing but not substitution), the practitioner gains a vocabulary for reasoning about *where* and *how much* two vocabularies disagree, and for tracking fidelity loss as it propagates across mapping hops under the weakest-link rule — questions that, absent the typed relation, cannot even be stated precisely.

manages_complexity:
Interoperating *n* separately governed schemes is, in the naive framing, a combinatorial mess: every scheme has its own concepts, its own hierarchy, its own update cycle, and the integrator who tries to make them queryable as one space faces either the politically impossible task of electing a single master vocabulary that everyone re-files into, or an undifferentiated tangle of crosswalk rows in which "diabetes here equals diabetes there" carries no record of whether that equality is safe. The schema mapping relation collapses that sprawl by reducing the entire question "how do these two vocabularies relate, and what may I do with the relationship?" to a small fixed tuple the analyst can read off directly: a source concept, a target concept, and one of a five-value correspondence grade. Once a bridge is filed as `exactMatch`, `closeMatch`, `broadMatch`, `narrowMatch`, or `relatedMatch`, the consumer no longer re-investigates the underlying clinical, bibliographic, or chemical semantics of each pairing — the grade already encodes the only thing the routing layer needs: how much fidelity the bridge guarantees. The high-dimensional "are these two concepts the same, and in what respects, and for whose purposes?" becomes a one-symbol lookup that the consumer's own use-requirement (substitute blindly / navigate with caution / climb the hierarchy only / browse but never infer) is matched against.

The branch structure the analyst tracks is exactly that grade ladder, and it is what makes the qualitative outcome legible without re-deriving each case. Reading `exactMatch` off a bridge, the analyst knows substitution is licensed and stops there; reading `closeMatch`, that a query depending on exact specificity must be flagged; reading `broadMatch` or `narrowMatch`, that inference may travel up the hierarchy but not down (or vice versa), foreclosing a whole class of invalid routings at a glance; reading `relatedMatch`, that the link serves browsing and any substitution built on it is a bug. Two further compressions ride on the same machinery. The *weakest-link* composition rule means a chain of bridges across schemes does not require re-evaluating the endpoints' semantics — the analyst propagates fidelity arithmetically, the lowest grade in the chain dominating, so an `exact`-then-`close` hop is known to be `close` without inspecting the concepts at all. And because each scheme stays sovereign while the bridges are first-class governed artifacts, the integrator tracks a single auxiliary quantity — the *gap inventory*, source concepts with no available target — that turns "where do these vocabularies fail to meet?" from a silent lookup miss into a countable, curatable signal. What would otherwise be an open-ended investigation of every concept-pair across every scheme reduces to: read the grade, check it against the use, propagate the weakest link, and watch the gap count.

abstract_reasoning:
The relation's foundational move *separates three facts a crosswalk row collapses* and reasons from a bridge's grade to which it claims: *correspondence* (a link exists at all), *interchangeability* (substitution is safe in this context), and *equivalence* (the bridge is loss-free for every purpose). The inference runs from the typed grade to the operative question of any bridge — *what fidelity does this claim, and is it sufficient for my use?* — rather than guessing a guarantee the assertion never made. Without the type every row reads as undifferentiated equality and a routing layer treats `closeMatch` and `exactMatch` alike, silently importing loss; the grade forces the three apart at declaration time so the consumer matches the grade against a use-requirement instead of assuming the strongest reading.

The distinctive umbrella move is *directional, inference-constraining* reasoning off the full grade ladder — the part that the exact-only and close-only siblings do not carry. Each grade encodes not just a fidelity level but a *direction and a permitted class of inference*, so reading the grade forecloses whole classes of invalid routing at a glance. `exactMatch` licenses blind substitution; `closeMatch` licenses navigation but flags that a query depending on exact specificity must be reviewed; `broadMatch` (c1 narrower than c2) licenses inference that climbs the hierarchy but *forbids descending* — a count rolled up to the coarser concept is valid, a claim pushed down to the finer one is not; `narrowMatch` is the mirror, licensing descent but not ascent; `relatedMatch` licenses browsing but *not* substitution or inference at all, so any substitution built on it is a bug. The reasoning therefore runs from the grade to a specific allowed-and-forbidden inference pattern, letting the analyst reject an invalid routing without re-inspecting the underlying clinical, bibliographic, or chemical semantics.

A *composition* move propagates fidelity across mapping hops by the weakest-link principle: a chain c1 `exactMatch` c2 `closeMatch` c3 yields c1-to-c3 at most `closeMatch`, so the analyst computes a multi-scheme route's guarantee arithmetically — the lowest grade in the chain dominates — and predicts where a long path will distort without examining the endpoint concepts. A *gap-as-signal* move treats the *absence* of a bridge as a first-class fact: a source concept with no available target is not a silent lookup miss but a countable curatorial signal about *where* and *how much* two sovereign vocabularies fail to meet, and tracking the gap inventory turns "do these schemes cover the same ground?" into a measurable property of their relationship. Finally, a *governance-and-drift* move reasons about the mapping table as its own versioned, revocable artifact distinct from either scheme's governance: a `closeMatch` may become `exactMatch` once one scheme catches up, a `broadMatch` reveals that the target vocabulary is coarser at this point than the source, and because the schemes stay sovereign while the bridges are independently reviewable, the analyst reasons about scheme drift, overlap, and disagreement by tracking changes to the bridges rather than re-aligning the vocabularies wholesale. All of this stays inside the interoperability substrate — it presupposes two separately-governed schemes joined by explicit typed assertions — so the grade-routing, directional-inference, weakest-link, gap, and drift inferences apply to terminology crosswalks, ontology alignment, ETL schema bridges, and regulatory equivalency, and are not exported to settings lacking sovereign schemes and a declared correspondence type.

knowledge_transfer:
Within knowledge organization and interoperability the schema mapping relation transfers *as full mechanism* — not just the shape but the typed grade ladder, the routing rules, the weakest-link composition, and the gap inventory carry intact across every subfield where two separately governed vocabularies must be joined. In **library and museum knowledge-organization systems** it is the home turf: SKOS mappings between thesauri (LCSH ↔ FAST), classification schemes (DDC ↔ UDC), and structured vocabularies (CIDOC CRM ↔ local catalogue terms) are filed at exactly these five grades, and a curator reads `broadMatch` off a local-term-to-LCSH bridge and knows inference may climb but not descend without re-investigating the subject semantics. In **healthcare terminology crosswalks** (ICD-9 ↔ ICD-10, ICD ↔ SNOMED-CT, LOINC ↔ local lab codes) the same `closeMatch`-flags-loss-risk discipline is load-bearing precisely because clinical and reimbursement consequences ride on the bridge's declared precision. In **bioinformatics ontology alignment** (Gene Ontology ↔ Disease Ontology, ChEBI ↔ DrugBank, cross-species orthology mappings) the graded-bridge-between-sovereign-catalogues structure recurs term-for-term. And in **data engineering** (ETL field mappings, schema bridges during migration, type-system bridges like `BigDecimal` ↔ JSON `number`) and **regulatory equivalency** (FDA ↔ EMA, SEC ↔ ESMA mutual-recognition declarations at a stated fidelity), the analyst routes a query or a record through the declared correspondence and propagates fidelity arithmetically across hops. Across all of these the vocabulary, the diagnostics, the maintainer's workflow (a mapping table is a versioned artifact with its own changelog; a near-match flag is a future-work marker; a missing mapping is a known gap), and the interventions transfer without translation — because they are all instances of one substrate: sovereign schemes joined by explicit typed assertions.

Beyond that interoperability substrate the case is **(B) — a shared abstract mechanism travels, but the entry's own named machinery does not.** What genuinely recurs across more distant domains is the *parent* structure: a typed correspondence between elements of two sets, graded for fidelity, with directional inference constraints. That is `function_mapping` plus a fidelity annotation, and it really co-instantiates wherever two formal systems are bridged — unit and currency conversion tables (kilograms ↔ pounds as a loss-free `exact`-with-factor bridge; time-keyed historical exchange rates as `close`), API translation layers between vendor SDKs, compiler type coercions. The general lesson that *does* carry is: when you bridge two independently governed representations, label each bridge with the fidelity it claims rather than treating every link as equivalence, and let the weakest grade dominate a chain. But the SKOS-specific cargo stays home-bound — the precise five-value ladder, the `skos:mappingRelation` sub-property semantics, the sovereign-scheme governance apparatus (assert / review / deprecate / replace as a separable concern from either scheme's own governance), and the gap-as-curatorial-signal practice are knowledge-organization furniture. Invoking "a schema mapping" for, say, two teams reconciling their internal jargon renames the components and borrows the grade-ladder *idea* while dropping the institutional machinery that makes the original a governable interoperability artifact rather than an ad-hoc analogy. The honest move when the structural lesson is needed cross-domain is to carry the parent — typed, graded `function_mapping` between sovereign systems — not the named library-science concept, whose apparatus is interoperability infrastructure rather than a substrate-independent pattern (see *Structural Core vs. Domain Accent*).


Scope Neglect

one_liner: Explain why elicited valuations stay flat across hundredfold changes in magnitude — the count is replaced by a single affect-laden prototype, so the number is understood but never priced in.

origin_subdomain: Valuation biases

core_idea:
Scope neglect is the empirical regularity, within judgment and decision-making, that the *valuation* a person attaches to an outcome is strikingly insensitive to the outcome's *magnitude*. Asked how much they would pay to save 2,000, 20,000, or 200,000 birds from oil-soaked deaths, respondents return willingness-to-pay figures statistically indistinguishable across a hundredfold range in scope. The number that *should* drive the answer — how many — slides past the judgment unmetabolized; what the valuation tracks instead is a single mental image, one oiled seabird, the *prototype* of the category. The affective response to that prototype, not the count behind it, sets the price.

The mechanism is representational and affective, not arithmetical. Encoded as a discrete, emotionally charged image, the cardinal quantity has *no place to land*: feelings are roughly bounded, and the distress evoked by one suffering animal versus ten thousand is far smaller than the ratio of the counts, because the imagery does not multiply. Kahneman and Frederick call this **attribute substitution** — a hard target attribute (scope-weighted value) is unconsciously replaced by an easier one (the prototype's affect), and the easy answer reported as if it answered the hard question.

Two finer commitments make it precise. **Embedding**: willingness to pay for a part (one region's lakes) is close to that for a superset (all the country's lakes), since both summon the same prototype — so valuations cannot be additive in scope. **Evaluability**: scope becomes usable only when magnitude is presented in comparable form, where the larger number does decision-relevant work; in *single evaluation* scope is nearly inert, in *joint evaluation* sensitivity partly returns. Scope neglect is thus not that people cannot count, but that under affect-heavy single-stimulus valuation the count is the wrong cue at the wrong time, and the prototype wins.

This is *not* general magnitude underweighting: Weber–Fechner compression keeps response *logarithmic* (still rising); scope neglect in its sharp form produces a *flat* curve — a decoupling specific to elicited valuation of affect-laden goods.

scope_of_application:
Scope neglect is a workhorse diagnostic across the valuation and risk subfields of JDM, and its reach is *within* that domain, not across substrates.

- **Contingent valuation in environmental economics** — the home turf. Surveys for protecting a species or watershed routinely fail the *scope test* (that willingness to pay rises with the quantity of the good); embedding (valuing "all the lakes" barely above "this lake") was the worry the NOAA Blue Ribbon Panel forced on the field, and scope sensitivity became its own validity criterion.
- **Charitable giving / the identifiable-victim literature.** Donations are flat, sometimes *decreasing*, in victim count when the appeal describes a group rather than one named individual — "psychophysical numbing."
- **Risk perception and public-health communication.** Risks of "thousands" versus "millions" elicit similar responses when the message centers a prototype victim; probability and magnitude are both neglected.
- **Public-goods valuation and policy support.** Support often fails to scale with stated reach: helping 100,000 draws support not much greater than helping 10,000, both keyed to the same prototype beneficiary.
- **Jury awards.** Punitive damages show outrage well-calibrated to *blameworthiness* but poorly to *dollar magnitude* (Kahneman, Schkade & Sunstein's "shared outrage, erratic awards").

structural_signature:
**Sig role-phrases:**

- *an affect-heavy outcome category summoned by a single prototype* (S) — the oiled bird, the starving child stands in for the class.
- *a valuation request in a magnitude-bearing currency* (S) — willingness to pay/accept or a rating that *ought* to scale with scope.
- *cardinal magnitude presented without comparative scaffolding* (S) — a raw count offered in single, isolated evaluation.
- *attribute substitution swaps scope-weighted value for prototype affect* (V) — the hard target attribute replaced by the accessible affective one.
- *the response curve comes out flat (or near-flat) in magnitude* (V) — equal valuations across order-of-magnitude scope differences.
- *part and superset valuations embed rather than add* (V) — willingness to pay for the whole barely exceeds that for a contained part.
- *scope sensitivity recoverable by joint / trade-off-forcing evaluation* (V) — explicit comparison restores partial proportionality.

what_it_is_not:
- **Not innumeracy.** Respondents giving flat valuations can state that 200,000 is a hundred times 2,000. The defect is not comprehending the magnitude but *recruiting* it — the number is understood, then unused.
- **Not measurement noise.** A noisy elicitation gives an *uncertain* slope; scope neglect gives one *reliably near zero*, replicating across studies and designs — a named effect, not a complaint about survey quality.
- **Not risk aversion or budget constraint.** The flatness is not "$80 is all they can spare" — it appears at every income level and for non-monetary ratings with no budget at all.
- **Not a law that valuation never responds to scope.** Under joint evaluation, conjoint trade-offs, or repeated exposure, sensitivity partly returns; neglect is *conditional* on affect-heavy, single-stimulus, no-comparison elicitation.
- **Not the part valued *more* than the whole.** Ordinary embedding gives part ≈ whole; a vivid part valued *above* its superset is a stronger, separate pathology (sign-reversed scope effect).

clarity: Naming scope neglect makes an otherwise-invisible failure mode legible. Without the label, a flat willingness-to-pay curve looks like a survey that "didn't work," inviting ad hoc explanations — bad sampling, hypothetical bias, protest responses. With it, the analyst asks a sharp structural question: *is the elicitation delivering magnitude in a usable form, or harvesting prototype-keyed affect?* That separates a genuine preference (the public really does value the larger program only slightly more) from a substitution artifact (the real preference masked because scope never entered) — and distinguishes three things survey practice blurs: *comprehension* (the respondent knows the number), *valuation* (the number drives the price), and *expression* (the format lets it show). Localizing the breakdown to the middle stage tells the designer that fixes aimed at the other two cannot move a slope whose problem is substitution.

manages_complexity: The stated-preference literature is a sprawl of anomalies — embedding, part–whole bias, ordering effects, probability insensitivity, the single–joint disparity. Scope neglect compresses a large slice into one regularity: *valuation under affect-heavy single evaluation is flat in scope*. A meta-analyst can then treat "scope sensitivity" as a single measurable quantity — the slope of valuation on magnitude — rather than re-deriving the pathology for every good and survey, and reason from two parameters (how affect-heavy the representation, whether the format forces comparison) to predict when the anomaly appears — collapsing a high-dimensional "which surveys misbehave" problem into a low-dimensional structural one.

abstract_reasoning: Scope neglect licenses three reasoning moves, all from the substitution mechanism. *Diagnostic*: facing a flat valuation, infer the respondent answered an easier affect-based question, and predict the co-occurring signature (embedding, single–joint disparity, probability insensitivity). *Interventionist*: to recover sensitivity, change the *form* of magnitude so the number acquires decision-relevant work — comparisons rather than absolute counts, conjoint trade-offs, a de-affectivized prototype; each is a prediction that the slope should *steepen*, and how much measures how much flatness was substitution. *Boundary-drawing*: a figure passing a scope test can enter cost–benefit analysis; one that fails cannot be read as scope-weighted value, and multiplying it by a large quantity yields a benefit estimate with no defensible relation to preference. The concept is a *validity gate* on a class of inferences.

knowledge_transfer:
Within JDM the diagnostic transfers cleanly and literally. Any elicitation asking for a numerical valuation of an affect-heavy outcome — environmental goods, charitable causes, health risks, public programs, legal damages — should be scope-tested, and the same fixes (joint evaluation, trade-off forcing, prototype de-emphasis) apply; the currency changes but the structure and remedies do not.

To *adjacent* fields transfer is real but partial, resting on shared mechanism. Marketing's price-evaluability work (Hsee) is the same attribute-substitution machinery on product attributes; behavioral public policy borrows scope neglect directly when warning that survey "public valuations" may be scope-insensitive.

Honesty requires marking where transfer becomes mere *analogy*. "Scope insensitivity" is invoked loosely for any response failing to scale with an input — evocative, but *not* the same mechanism (no elicited valuation, no affect-keyed prototype, no embedding to test). Outside elicited valuation of affect-laden outcomes the phenomenon dissolves into its neighbors (affect heuristic, prototype reasoning, identifiable-victim effects, logarithmic perception) or fails to reproduce: the within-domain transfer is substantive, the cross-domain one metaphor.


Screen-Reader Ambiguity

one_liner: An interface's visual structure carries semantic information never encoded into the machine-readable accessibility tree, so an assistive-technology user — navigating only that projection — encounters an interface stripped of the organization a sighted user relies on.

core_idea:
Screen-reader ambiguity is the accessibility-engineering pathology in which a user interface's visual structure — headings, lists, tables, regions, buttons versus links, form-field grouping, state changes, error messages — carries semantic information that has not been encoded into the machine-readable layer the assistive technologies consume. A sighted user navigates the interface by visual affordance; a screen-reader user navigates by those same affordances *as projected through the accessibility tree* — the structured representation that browsers, native platforms, and document formats expose via accessibility APIs (ARIA, UIKit Accessibility, UI Automation, AT-SPI, tagged PDF). When the accessibility tree is absent, ambiguous, or wrong relative to the visual structure, the assistive-technology user encounters an interface stripped of the organization their sighted counterpart navigates by: section headers rendered as unsemantic `<div>` elements cannot be jumped between; progress indicators encoded only as styled `<span>` sequences carry no "current step" state; validation error messages that appear visually in red below a field but are not associated with it via `aria-describedby` cannot be discovered by a user who cannot see where the visual error marker has appeared.

The structural commitment the pathology violates is that an interface has two parallel representations — the visual-presentational rendering and the semantic-structural accessibility tree — and that both must carry the same structural information. Undermaintaining the second relative to the first does not produce a visible defect in the design tool or the browser; it produces exclusion, error, and unrecoverable workflow breaks that are invisible to anyone who does not test with assistive technology. The corresponding intervention family is authoring and maintaining the semantic layer: semantic HTML element choice over styled divs, ARIA role and property annotation where native semantics are insufficient, programmatic name and state exposure in native frameworks, tagged structure in document formats. The diagnostic method is accessibility-tree inspection and assistive-technology testing, not visual review.

scope_of_application:
Screen-reader ambiguity lives wherever a digital interface has a visual rendering plus a machine-consumable accessibility-tree projection that assistive technology reads — one domain, accessibility engineering, spanning every platform and regulated setting that exposes such a tree. Calling a data feed "screen-reader-ambiguous" because its structure is buried in presentation borrows the shape; the genuine layer-mismatch lesson there belongs to the broader presentation-versus-semantic-layer pattern, and stays off the map below.

- **Web accessibility.** The dominant authoring discipline: WCAG 2.x, ARIA authoring practices, the role-name-state contract, and automated checks (axe-core) against `div`-as-header, unannounced errors, and missing current-step state.
- **Native mobile applications.** iOS UIKit Accessibility and Android AccessibilityService, where programmatic name/role/state must match the visual hierarchy for VoiceOver and TalkBack.
- **Desktop applications.** Microsoft UI Automation and Linux AT-SPI, including the long tail of legacy GUI toolkits shipped with no accessibility layer at all.
- **Document formats.** Tagged PDF and accessible EPUB as the document-format analogues — headings, columns, footnotes, and tables that require explicit semantic tagging for screen-reader consumption.
- **Medical-device interfaces.** Infusion pumps, ventilators, and diagnostic equipment whose touch panels often lack accessibility-tree exposure, under FDA human-factors guidance and ISO 62366.
- **Public-service forms.** Government tax-filing and benefits applications whose semantic-layer shortfalls produce systematic exclusion of assistive-technology users.
- **Education platforms and industrial HMIs.** LMS video, math notation, and interactive content lacking the needed semantic layer, plus control-room consoles with no API for assistive-technology consumption as operator accessibility becomes a workplace-equity concern.

structural_signature:
**Sig role-phrases:**

- **the visual representation** — the rendered interface whose structure (headings, lists, tables, regions, buttons-vs-links, state) sighted users navigate by affordance
- **the semantic accessibility tree** — the parallel machine-readable projection (ARIA roles, name/role/state, tagged structure) that assistive technologies consume
- **the assistive technology** — the screen reader, braille display, or voice control that re-presents the interface from the semantic layer alone
- **the user task** — the workflow that requires the projected structure to be correct to be completable
- **the divergence** — the semantic layer absent, ambiguous, or wrong relative to the visual one: the single defect type every failure in the catalogue instances
- **the silent character** — no warning in design tool or browser, since the gap is exactly between what is seen and what is encoded; detectable only by accessibility-tree inspection and AT testing
- **the semantic-layer authoring fix** — the one remediation family (semantic element over styled div, ARIA role/state, programmatic name, tagged structure), pixel-level fixes ruled out as unable to touch the unrendered layer

what_it_is_not:
- **Not a visible-presentation defect.** Low contrast, undersized touch targets, missing captions, and motion sensitivity are all properties of the *seen* interface, fixed by adjusting pixels. Screen-reader ambiguity is categorically different: the visible design can be flawless and the defect total, because what is absent or wrong is the *semantic layer* the user never sees rendered. Pixel-level fixes cannot touch it.
- **Not certified by a good-looking interface.** That an interface renders cleanly and behaves correctly on screen says nothing about whether the accessibility tree exposes the headings, states, and associations the visual structure encodes. A visually polished, semantically empty page is the characteristic case — the failure is silent to anyone not testing with assistive technology, producing no warning in the design tool or browser.
- **Not information asymmetry.** The assistive-technology user does not lack the information *in principle*; they lack the *encoding* the interface chose to provide only visually. The defect is a representational gap in the authored markup, not an unequal distribution of knowledge between parties — the same content is simply unexposed to the machine-readable layer.
- **Not detectable by visual inspection.** Because the gap is precisely the gap between what is seen and what is encoded, visual review inspects exactly the layer that is *not* at fault and reports nothing wrong. Only accessibility-tree inspection and actual assistive-technology testing can detect it; a clean visual pass should raise suspicion, not retire it.
- **Not any presentation-versus-semantic-layer mismatch.** The general kernel — structure carried in presentation but not in the machine-consumable model — recurs in spreadsheets keyed to cell formatting or reports structured by whitespace, but those are co-instances of the broader layer-mismatch pattern. Screen-reader ambiguity is the instance bound to assistive technology; calling a data feed "screen-reader-ambiguous" borrows the shape while the ARIA-and-accessibility-tree mechanism stays in HCI.

clarity:
Naming screen-reader ambiguity separates a class of accessibility defect that "the page is inaccessible" lumps together with problems it has nothing in common with. Low contrast, undersized touch targets, missing captions, and motion sensitivity are all properties of the *visible* presentation — they are seen and fixed by adjusting what is on screen. Screen-reader ambiguity is categorically different: the visible interface can be flawless and the defect still total, because what is missing or wrong is the *semantic layer* the user never sees rendered. The label pins that distinction, and it tells the remediator to look in the place visual review cannot reach — the accessibility tree — and to fix the markup contract (semantic element choice, ARIA role and state, programmatic name) rather than anything about the pixels. A team that has not drawn this line keeps adjusting the visible design and never moves a defect whose home is the machine-readable projection.

The frame's sharper payoff is that it makes "is this interface accessible?" a decidable, decomposed question instead of a verdict. Once the interface is understood as two parallel representations that must carry the same structure, the audit splits cleanly into three checks: *does a semantic representation exist* for this element at all (the unsemantic `<div>` header), *is it correct* relative to the visual structure (the unassociated error message, the missing current-step state), and *does the assistive technology consume it correctly*. That decomposition organizes both authoring and audit, and it pre-empts the field's characteristic blind spot — that the failure is silent to everyone who does not test with an actual screen reader, producing no warning in the design tool or the browser. So the concept also fixes the diagnostic method: the only test that detects it is accessibility-tree inspection and assistive-technology use, never visual inspection, because the gap it names is precisely the gap between what is seen and what is encoded.

manages_complexity: The sprawl this concept compresses is a large, heterogeneous catalogue of accessibility failures — headers that cannot be jumped between, progress indicators with no current-step state, validation errors that go unannounced, controls activated in the wrong order because labels are misordered, tables read as undifferentiated cell streams, regions with no landmarks — each surfacing in a different element, on a different platform (web ARIA, iOS UIKit Accessibility, Windows UI Automation, AT-SPI, tagged PDF), with its own remediation lore. Taken one by one they look like an open-ended bug list with no shared structure, and they share none of their surface with the *visible* accessibility defects (contrast, target size, captions) they are filed alongside. Screen-reader ambiguity collapses that catalogue onto a single schema: two parallel representations of one interface — the visual-presentational rendering and the semantic-structural accessibility tree — that must carry the same structural information, plus the assistive technology that consumes the second and the user task that needs the mapping correct. Every failure in the catalogue is then one instance of a single defect type: the semantic representation diverging from the visual one. What the auditor tracks per element shrinks to that one relation rather than a per-symptom rulebook, and the qualitative outcome — usable or stripped-of-structure — reads off whether the machine-readable layer matches what the visual layer encodes. Because the divergence is the *only* variable that matters, two further simplifications follow directly. The remediation space narrows to one family — author and maintain the semantic layer (semantic element over styled div, ARIA role and state, programmatic name, tagged structure) — so the team stops reaching for pixel-level fixes that cannot touch a defect whose home is the unrendered layer. And the diagnostic method is fixed by the schema: since the gap is precisely the gap between what is seen and what is encoded, only accessibility-tree inspection and assistive-technology use can detect it, never visual review. A high-dimensional "which of these many accessibility failures, on which platform, is this?" reduces to one schema and one tracked relation: does the semantic representation exist, and does it match the visual one, for this element?

abstract_reasoning:
Screen-reader ambiguity licenses an accessibility-engineering reasoning kit built on the dual-representation schema — a visual rendering and a semantic accessibility tree that must carry the same structure — and one tracked relation between them.

*Diagnostic — classify a defect as semantic-layer, not visible-presentation.* The signature inference runs FROM the kind of accessibility failure TO where it lives. Reason FROM a flaw that persists even when the visible interface is flawless TO the conclusion that the defect is in the unrendered semantic layer, categorically different from contrast, target size, captions, or motion sensitivity, which are properties of the visible presentation. This tells the remediator to look where visual review cannot reach — the accessibility tree — and to fix the markup contract (semantic element choice, ARIA role and state, programmatic name) rather than anything about the pixels, so a team stops adjusting the visible design and never moving a defect whose home is the machine-readable projection.

*Diagnostic — decompose accessibility into three checks per element.* The move turns "is this interface accessible?" from a verdict into a decidable, decomposed question. Reason FROM the dual-representation schema TO three sequential checks for each element: does a semantic representation exist at all (the unsemantic `<div>` header), is it correct relative to the visual structure (the unassociated error message, the missing current-step state), and does the assistive technology consume it correctly. The single tracked relation — does the semantic representation match what the visual layer encodes — is read per element, and the qualitative outcome (usable or stripped of structure) follows from that one relation rather than a per-symptom rulebook.

*Diagnostic — infer the silent, AT-only-detectable character of the failure.* The move reasons FROM the nature of the gap — precisely the gap between what is seen and what is encoded — TO the conclusion that the failure is silent to everyone who does not test with an actual screen reader, producing no warning in the design tool or the browser. This fixes the diagnostic method by inference: only accessibility-tree inspection and assistive-technology use can detect it, never visual inspection, because visual inspection sees exactly the layer that is not at fault. Reason FROM "the design tool and browser report nothing wrong" TO "the defect may still be total," guarding against the field's characteristic blind spot.

*Interventionist — author the semantic layer to match the visual structure.* Because the divergence is the only variable that matters, the corrective move narrows to one family: reason FROM the specific failed check TO the matching authoring fix — a semantic element over a styled div where no representation exists, an ARIA role and state or programmatic name where it is incorrect, tagged structure in a document format — each predicted to make the machine-readable layer carry what the visual layer encodes. The intervention is selected by which of the three checks failed and verified by re-inspecting the tree with assistive technology, since the schema rules pixel-level fixes out as incapable of touching a defect in the unrendered layer.

knowledge_transfer:
Within accessibility engineering the concept transfers *as mechanism*, and this is its operating range. The dual-representation schema, the three-checks decomposition (does a semantic representation exist, is it correct, does the assistive technology consume it), and the accessibility-tree-not-visual-review diagnostic method carry without translation across every platform that exposes an accessibility tree: web (ARIA, axe-core, the WCAG role-name-state contract), native mobile (iOS UIKit Accessibility, Android AccessibilityService), desktop (Microsoft UI Automation, AT-SPI), and the document-format analogues (tagged PDF, accessible EPUB), as well as the regulated settings that consume these same APIs — medical-device interfaces under FDA human-factors and ISO 62366, public-service forms, education platforms, industrial HMIs. The authoring discipline (semantic element over styled div, ARIA role and state where native semantics fall short, programmatic name in native frameworks, tagged structure in documents) and the role-name-state framing port between them as engineering, because all of these are the *same* substrate: a digital interface with a visual rendering plus a machine-consumable accessibility-tree projection. The vocabulary travels with the diagnosis throughout this domain precisely because the thing it names — a semantic layer diverging from the visual one — is literally present wherever assistive technology consumes an interface.

Beyond that substrate the transfer is the third category rather than mechanism or bare metaphor: a more general pattern genuinely recurs, but this entry's *own* machinery stays home. The substrate-neutral kernel is *a presentation layer carrying structural information that is not encoded in the underlying machine-consumable representation, so a consumer that reads only the model — not the rendering — recovers the wrong structure or none.* That really recurs across domains as co-instances: a spreadsheet whose meaning lives in cell *formatting* rather than in formulas or typed values, so a program reading the model misses it; a legacy report whose structure is visual whitespace a parser cannot recover; a document whose heading hierarchy exists only in font size, not in the semantic model a downstream pipeline walks. Where the cross-domain lesson is needed it should be carried by that general presentation-versus-semantic-layer mismatch pattern — closest in the present catalogue to `interpretation` (recovering meaning from a representational substrate), with the cleaner parent (`presentational_semantic_mismatch`) not yet a prime — and *not* by "screen-reader ambiguity" as named, because its cargo (ARIA, the accessibility tree, screen readers and braille displays, WCAG conformance, axe-core) is accessibility-engineering furniture with no referent once there is no assistive technology and no human navigating an interface. So to say a data feed is "screen-reader-ambiguous" because its structure is buried in presentation is to borrow the shape while the AT-specific mechanism stays in HCI; the honest move is to mark that as analogy and route the real lesson to the general layer-mismatch pattern. What is genuinely substrate-spanning here is that general pattern; what is distinctive to this entry is bound to the assistive-technology substrate — exactly the split *Structural Core vs. Domain Accent* draws.