Market Pull

one_liner: The innovation situation in which articulated demand-side need — customers naming a problem and willing to pay — directs the search of developers and investors and pulls solutions into existence; the demand-side pole of the push/pull dichotomy, keyed to where the binding constraint sits.

core_idea:
Market pull is the innovation-studies term for the situation in which articulated demand-side need — customers or users naming a problem they want solved, an unmet requirement, an explicit willingness to pay — directs the search of developers, scientists, and investors and pulls solutions into existence. The term is the demand-side half of a doctrinal dichotomy whose complementary half is technology push, in which a new capability searches for problems it can address; the dichotomy, developed in the innovation-studies literature from Schmookler (1966) forward, names the two ends of a spectrum defined by where the binding constraint in an innovation episode is located — on the demand side (recognised need that exceeds available solutions) or the supply side (available capability that exceeds recognised applications for it).

The construct encodes a specific causal claim: that in demand-constrained innovation episodes, organising R&D effort, funding allocation, and search activity around articulated need is more efficient than organising it around available technology. The mechanism is selection-pressure exerted by the demand environment on a variant-producing search process: money, attention, and status flow from the demand side toward solutions that fit the articulated need, sorting candidate solutions and reinforcing those that match. The doctrinal limitation is equally precise: market pull is blind to latent demand — needs that customers cannot yet articulate because the solution form is not yet imaginable — making it unsuitable as the organising principle where the relevant innovation bottleneck is upstream basic science or where the demand is pre-articulable. The pull/push choice is therefore not a universal preference but a diagnostic question about where the binding constraint in a specific innovation situation lies.

scope_of_application:
Market pull lives across the innovation-studies and R&D-management subfields wherever a variant-producing innovation search can be steered by articulated demand; its reach is within that substrate, the wide spread below being genuine co-instances of one organizing doctrine rather than cross-domain travel. The substrate-independent mechanism it specializes — a directional pressure sorting variants by fit — belongs to the parent primes (`selection_pressure` + `feedback`), not to this map.

- **Innovation studies and technology management** — the canonical home: the demand- versus supply-induced-invention literature from Schmookler (1966) forward, and the push/pull framing in innovation-management practice.
- **Product management** — the dominant frame in consumer software and hardware: customer interviews, jobs-to-be-done, and voice-of-the-customer programmes are all market-pull instruments directing the development search.
- **Public-research funding** — the curiosity-driven (push) versus mission-oriented or challenge-driven (pull) distinction, with pull programmes setting specific problems (a cancer moonshot, carbon-capture prizes, DARPA challenges) and inviting solutions.
- **Public-services and civic design** — human-centred-design programmes for social services, civic tech, and public health that start from articulated citizen need and pull capability toward it.
- **Pharmaceutical R&D** — advance market commitments and disease-specific prize programmes as pull instruments, set against basic-research grants and target-of-opportunity development on the push side.
- **Defence procurement** — warfighter-stated requirements (pull) versus laboratory-developed capabilities seeking applications (push), the same dichotomy under different vocabulary.
- **Agricultural extension and rural development** — farmer-articulated problem statements pulling research toward applied solutions, in contrast to research stations pushing technologies developed on-station.

structural_signature:
**Sig role-phrases:**

- **the articulated demand-side need** — customers or users naming a recognized, prioritized problem, unmet requirement, or explicit willingness to pay
- **the directable search substrate** — the variant-producing R&D, capability-development, or service-design effort whose direction can be steered
- **the demand-side binding constraint** — the diagnostic condition that defines pull's home: recognized need exceeds available solutions (versus push, where capability exceeds recognized applications)
- **the pull mechanism** — money, attention, and status flowing from the demand side toward solutions, sorting candidate solutions by fit-to-need and reinforcing the matching ones
- **the push/pull dichotomy** — the doctrinal frame that gives the construct its operational use, naming the two ends of the constraint-locus spectrum
- **the typed instrument family** — the pull-side instruments (jobs-to-be-done, voice-of-customer, advance market commitments, challenge funds) the doctrine deploys, each with a known suitability profile
- **the latent-demand blind spot** — the construct's principled limitation: pull is structurally blind to need customers cannot articulate because the solution form is not yet imaginable
- **the situation-specific diagnosis** — pull is not a standing preference but an episode-by-episode reading of where the binding constraint and articulability sit

what_it_is_not:
- **Not a standing preference for being "customer-driven."** Market pull is not a blanket endorsement of demand-led innovation; it is a *situation-specific diagnosis* about where the binding constraint sits. Pull is correct only in demand-constrained episodes (recognized need exceeding available solutions) — treating it as always-right is the error the dichotomy exists to prevent, since supply-constrained episodes call for push.
- **Not the inferior half of the dichotomy.** Technology push is not a mistake that pull corrects; it is the right organizing mode where available capability exceeds recognized applications or the bottleneck is upstream basic science. The pair names two ends of a constraint-locus spectrum, and a well-run portfolio *splits* by locus rather than choosing pull globally.
- **Not free of blind spots.** Because the pull mechanism sorts solutions by fit to *articulated* demand, it is structurally blind to *latent* demand — need customers cannot name because the solution form is not yet imaginable. A pull-organized effort aimed at a pre-articulable need under-delivers no matter how well executed; the selecting environment has nothing articulated to select on.
- **Not the same as its instruments.** Customer interviews, jobs-to-be-done, voice-of-the-customer programmes, advance market commitments, and challenge funds are pull-side *instruments* that instantiate the doctrine; market pull is the organizing principle they serve. The doctrine is the direction-of-search choice, not any one method for eliciting need.
- **Not a new sorting mechanism.** The machinery — a directional environmental pressure that sorts variants by fit and reinforces the better-fitting through returned signal — is general selection pressure plus feedback. Market pull's novelty is the *demand-side-as-selecting-environment* framing plus the push/pull apparatus and latent-demand caveat, not a distinct mechanism of selection.

clarity:
Naming market pull, and its doctrinal partner technology push, makes legible that an innovation effort has a *direction of organisation* at all — a choice that otherwise stays implicit and so goes unexamined. Without the dichotomy, "we do R&D" hides whether the search is being steered by articulated need or by available capability, and the two conflate into predictable strategic failures: a push-organised programme that imagines itself need-led produces capability in search of an application, while a pull-organised programme blind to what is technically reachable produces articulated requirements no one can yet satisfy and frustrated stakeholders. Giving each mode a name turns a portfolio into something diagnosable — *is this effort research-led or need-led, and is that organising mode matched to where the binding constraint actually lies?* — and reframes the central question from a standing preference ("are we customer-driven?") into a situation-specific one about the locus of the constraint: demand that exceeds available solutions, or capability that exceeds recognised applications.

Its second clarifying force is to make the construct's own limit precise rather than latent. By naming the pull mechanism — money, attention, and status flowing from articulated demand to sort candidate solutions by fit — the framework also makes visible exactly what that mechanism cannot see: *latent demand*, need that customers cannot articulate because the solution form is not yet imaginable. Pull is structurally blind there, and stating so converts a vague intuition ("listening to customers sometimes misses the big thing") into a sharp boundary condition a programme designer can act on — deploy pull where need is articulable and the bottleneck is downstream, switch to push or discovery-led instruments where the bottleneck is upstream basic science or the demand is pre-articulable. The dichotomy thus does double duty: it sorts the available organising modes *and* tells a practitioner the one situation in which the demand-side mode systematically fails.

manages_complexity: Deciding how to organize an innovation effort — a research portfolio, a funding mechanism, a product team's search — otherwise confronts an open field of considerations: the maturity of the relevant science, the depth of customer need, the state of available capability, the competitive landscape, the time horizon, the instruments on hand (grants, prizes, customer interviews, advance market commitments, requirements processes). Market pull, as one pole of the push/pull dichotomy, compresses that field onto a single diagnostic axis: where does the binding constraint in this innovation episode sit — on the demand side (recognized need exceeding available solutions) or the supply side (available capability exceeding recognized applications)? Collapsing the organizing decision to that one locus-of-constraint parameter lets the programme designer read the appropriate mode off it rather than re-deriving strategy from the full situation: demand-constrained episodes call for organizing R&D, funding, and search around articulated need (pull); supply-constrained episodes call for organizing around available capability (push). The dichotomy further discretizes the continuum into a small set of typed organizing modes — pure pull, pure push, demand-articulation programmes, challenge-driven funding, advance market commitments, push-then-match — each carrying a known suitability profile and characteristic failure mode, so the designer selects among types instead of inventing an organizing scheme from scratch and can split a portfolio by sorting each sub-area to its constraint locus. The compression also fixes the one branch where the demand-side mode systematically fails: latent demand, need customers cannot articulate because the solution form is not yet imaginable, to which pull is structurally blind. Naming that blind spot turns a vague caution into a hard boundary condition the designer tracks as one more parameter — is the need articulable, or pre-articulable? — switching to push or discovery-led instruments exactly where pull would mislead. What was an open, multi-factor "how should we organize the search?" becomes a low-dimensional diagnosis: locate the binding constraint, match the organizing mode, and check the single articulable-versus-latent condition that bounds the demand-side choice.

abstract_reasoning:
Market pull licenses a set of reasoning moves built on two structural facts: that it is one pole of a dichotomy keyed to *where the binding constraint sits* (demand side versus supply side), and that its mechanism — articulated demand sorting candidate solutions by fit — is *structurally blind to latent demand*.

*Diagnostic — locate the binding constraint, and classify an innovation effort as need-led or research-led off that locus.* The characteristic inference reduces the organizing decision to a single axis: in this innovation episode, does the binding constraint sit on the demand side (recognized need exceeding available solutions) or the supply side (available capability exceeding recognized applications)? The move runs from that one reading to a classification of the effort — demand-constrained episodes are diagnosed as calling for organization around articulated need (pull), supply-constrained episodes as calling for organization around available capability (push). The corollary diagnostic detects the conflation the dichotomy exists to expose: a programme that imagines itself need-led but is actually steered by available capability produces capability in search of an application; a programme blind to what is technically reachable produces articulated requirements no one can yet satisfy. So the analyst reads a portfolio's *direction of organization* — research-led or need-led — and checks whether that mode matches where the constraint actually lies, rather than accepting "we do R&D" as if it had no direction.

*Diagnostic of the mechanism's blind spot — distinguish articulable from latent demand, and predict where pull systematically fails.* The concept's sharpest move makes the construct's own limit precise. Because the pull mechanism works by money, attention, and status flowing from *articulated* demand to sort solutions by fit, it cannot see *latent* demand — need customers cannot name because the solution form is not yet imaginable. The inference is a boundary condition: where the relevant need is pre-articulable, or the bottleneck is upstream basic science, pull is structurally blind and will systematically miss the opportunity. So the analyst asks one more question — is the need articulable, or latent? — and predicts that a pull-organized effort aimed at a latent need will under-deliver no matter how well executed, because the selecting environment has nothing articulated to select on. This converts a vague caution ("listening to customers sometimes misses the big thing") into a sharp, actable failure prediction tied to the articulability of the demand.

*Interventionist — match the organizing mode to the constraint, split a portfolio by locus, and switch instruments where pull is blind.* The corrective reasoning follows from the diagnosis. Where the constraint is recognized, articulable need with a downstream bottleneck, deploy pull instruments — challenge funds, advance market commitments, voice-of-customer and jobs-to-be-done programmes, requirements processes — predicting that demand-side selection pressure will sort solutions toward fit. Where the bottleneck is upstream basic science or the demand is pre-articulable, switch to push or discovery-led instruments (open-ended investigator grants, ethnographic discovery, speculative prototyping), predicting that pull would mislead. The portfolio move is to *split* by constraint locus rather than choose one mode globally: route translational and applied sub-areas (with known targets) to pull, basic-research sub-areas (with undiscovered mechanisms) to push. Each typed mode — pure pull, pure push, demand-articulation programme, push-then-match — carries a known suitability profile and characteristic failure, so the designer selects among types and predicts each one's outcome from the constraint it is matched to.

*Boundary-drawing — separate the organizing doctrine from its instruments and its underlying mechanism, and treat pull as a situation-specific diagnosis rather than a standing preference.* Several lines the concept draws. First, market pull is an *organizing doctrine*, distinct from the pull-side methodologies that instantiate it (customer interviews, jobs-to-be-done, advance market commitments) — so the move is to reason about the direction-of-search choice, not to confuse the doctrine with any one instrument that serves it. Second, its mechanism is a specialization of the general process by which a directional environmental pressure sorts variants by fit and reinforces the better-fitting through returned signal — so the move is to recognize that the *novel* content here is the demand-side-as-selecting-environment framing plus the latent-demand caveat and the push/pull apparatus, not a new sorting mechanism. Third, and most load-bearing: the pull/push choice is *not* a universal preference ("are we customer-driven?") but a situation-specific diagnostic about the locus of the binding constraint — so the move is to refuse to treat pull as always-correct and instead re-ask, per episode, whether demand or capability is the binding constraint and whether the demand is articulable. Drawing these boundaries keeps the analysis fixed on the constraint-locus diagnosis and its one principled exception, rather than collapsing into a blanket endorsement of demand-led innovation.

knowledge_transfer:
Within innovation studies and R&D management the construct transfers *as mechanism*, and its reach across the innovation substrate is wide because the same locus-of-constraint diagnostic and the same push/pull instrument family recur wherever effort can be directed by articulated need. From product management (customer interviews, jobs-to-be-done, voice-of-the-customer programmes) to public-research funding (mission-oriented and challenge-driven programmes, prizes, DARPA-style challenges) to pharmaceutical R&D (advance market commitments, disease-specific prizes) to defence procurement (warfighter-stated requirements versus laboratory capabilities seeking applications) to agricultural extension (farmer-led priority-setting versus station-led trials), the dichotomy, the typed organizing modes, and the latent-demand boundary condition all carry without translation — defence procurement's requirements-versus-lab-capability framing is recognizably "the same dichotomy under different vocabulary." These are not analogies but co-instances of one organizing doctrine: each is literally a variant-producing innovation search being steered by where its binding constraint sits.

Beyond the innovation substrate the honest reading is the shared-abstract-mechanism case, and the entry's own structure makes it clean. What genuinely recurs across domains is not "market pull" but the *general mechanism it specializes*, already catalogued at the prime level: a directional environmental pressure that sorts variants by fit and reinforces the better-fitting through returned signal — `selection_pressure` composed with `feedback`. Market pull is exactly that pressure with *articulated demand* as the selecting environment and an *innovation search* as the substrate. That parent travels through biology, markets, and learning systems on its own; it is where any cross-domain lesson about demand-shaped search lives. What stays home-bound is the innovation-studies *normative apparatus* layered on top: the demand-side/supply-side dichotomy with technology push, the suite of pull instruments (JTBD, voice-of-customer, advance market commitments, challenge funds), and the latent-demand caveat as a doctrinal limitation. So even the wide within-substrate spread is, mechanically, a *vocabulary-and-instrument port* of one selection-via-articulated-demand pattern rather than the arrival of a new mechanism; and off the innovation substrate entirely, the disciplined move is to carry the `selection_pressure`/`feedback` parents (a directional signal sorting variants, blind to whatever signal is not emitted), not to import "market pull" with its push/pull doctrine and instrument suite. The named construct earns its keep where there is an innovation search to organize by articulated need, and its doctrinal cargo — including the principled latent-demand exception — is the part that does not generalize past that substrate. (See *Structural Core vs. Domain Accent*.)


Martha Mitchell effect

one_liner: Diagnose the trap in which an evaluator discounts a true but improbable report as delusional purely on its low prior probability, never testing whether its external referent is real.

status: draft

core_idea:
The Martha Mitchell effect, named by Brendan Maher (1988), is the clinical and diagnostic pattern in which a person's accurate report of an unusual real event is misclassified as delusional — precisely because the report's content is implausible. The name comes from Martha Mitchell, wife of US Attorney General John Mitchell, who in 1972 telephoned journalists to report that Nixon-administration officials were conducting illegal political operations and that she was being held against her will; she was widely treated as mentally ill until the Watergate revelations vindicated her claims entirely.

The structural mechanism is base-rate over-weighting in credibility evaluation. When a claim has a low prior probability and the claimant carries any marker that raises the prior for psychopathology — prior psychiatric contact, social isolation, intoxication, minority status, or vulnerability relative to the named perpetrators — the evaluator discounts the claim on the basis of prior probability alone, without testing the claim's external referent. If the prior happens to be wrong because the claim is true, the evaluator's error is predictable and systematic: a content-only diagnostic routine, one that examines only how plausible the claim sounds rather than whether its referent can be independently verified, will reliably misclassify true low-prior reports as confabulation. The trap is self-reinforcing: once the initial misclassification is made, subsequent protests by the claimant are absorbed as further evidence for the diagnosis, so the loop cannot be broken from inside. Vindication requires a mechanism external to the original evaluation — an independent complaint, physical evidence, or a corroborating witness — that circumvents the loop entirely.

scope_of_application:
The Martha Mitchell effect lives across the credibility-evaluation subfields of clinical and forensic psychology — wherever an evaluator triages a low-prior claim under adverse priors on a content-only routine that never tests the referent — and beyond that cluster the *pattern* recurs but the name does not (in courts, journalism, and intelligence the same corrective travels as corroboration requirement, due process, or fact-check, carried by its Bayesian and epistemic-injustice parents).

- **Psychiatric diagnosis** — the home turf and the teaching case for "thinking past the prior": checking whether an unusual report has external corroboration before filing it as a delusion of persecution.
- **Forensic evaluation** — reports by detained persons of staff misconduct, surveillance, or institutional abuse sit at elevated risk of Martha-Mitchell misclassification, since the claimant's status sharpens the prior against them.
- **Trauma and coercive-control contexts** — victims of stalking, gaslighting, or institutional abuse who report true but improbable persecution by a higher-status perpetrator are especially prone to pathologization.
- **Whistleblowing and intelligence settings** — genuine reports of covert operations are cited as Martha-Mitchell cases when the reporter is pathologized before independent verification arrives.

structural_signature:
**Sig role-phrases:**

- **the low-prior claimant** — a person reporting a true but improbable event whose prior probability is low under the evaluator's working model
- **the role-pressured evaluator** — a clinician or institution rewarded for triaging cases efficiently on the prior
- **the content-only routine** — a diagnostic procedure that weighs the claim's plausibility and the claimant's presentation but never tests the external referent
- **the pathologization markers** — claimant features (prior psychiatric contact, isolation, intoxication, low status vs. the named perpetrator) that further sharpen the prior
- **the base-rate over-weighting** — low prior probability mistaken for positive evidence of falsity, collapsing the posterior onto the prior
- **the misclassification event** — the true claim filed as confabulation/delusion
- **the self-sealing trap** — each protest is absorbed as fresh symptom, raising the very prior it should lower, so the loop cannot break from inside
- **the external vindication** — only a mechanism the original evaluation never consulted (independent complaint, physical evidence, corroborating witness, time) can break it

what_it_is_not:
- **Not base-rate neglect.** It is the *directional inverse*: base-rate neglect under-weights the prior; the Martha Mitchell error over-weights it, collapsing the posterior onto a low prior and discounting the claim on improbability alone. The defect is leaning too hard on the prior, not ignoring it.
- **Not gaslighting.** Gaslighting requires an adversary deliberately undermining the claimant's grip on reality. Here there is no adversarial intent: the evaluator's prior does the work on its own, and a conscientious clinician applying a content-only routine produces the misclassification without meaning the claimant any harm. The mechanism is structural, not manipulative.
- **Not a claim that implausible reports are usually true.** The effect does not invert the base rate or counsel credulity; most low-prior claims really are false. Its narrow point is that improbability is not *refutation* — low prior probability has been mistaken for positive evidence of falsity — so a true low-prior report is systematically misfiled unless its referent is tested.
- **Not identical to epistemic injustice.** The systematic credibility discount can compound with identity-based prejudice, but the Martha Mitchell error is *prior-content-driven*: it is the implausibility of the claim, not the social identity of the claimant, that collapses the judgment. The two frequently co-occur, yet the effect can arise with no group-based discount at all.
- **Not a verdict on the claim's content.** Diagnosing the effect says nothing about whether the report is in fact true; it identifies a *procedural* gap — that the external referent was never tested, only the content re-examined. The corrective is verification, not a presumption of truth, because from inside a content-only routine a true low-prior report and a genuine confabulation are indistinguishable.

clarity:
Naming the Martha Mitchell effect makes visible a failure mode that hides inside ordinary differential diagnosis: that a content-only routine renders "delusion" effectively *unfalsifiable*. If the only evidence weighed is how implausible the claim sounds and how the claimant presents, then a true low-prior report and a genuine confabulation look identical from inside the consulting room — and the claimant's protests, which ought to be data, get re-read as worsening symptoms. The label forces the one question the routine omits: did anyone test the claim's *external referent* — was the named staff member present at the reported times, did a separate witness corroborate — rather than re-examine the claim's *content*? That reframing turns an invisible structural trap into a checkable procedural gap.

It also sharpens a distinction the bare notion of "diagnostic error" blurs: the difference between discounting a claim because it has been *shown false* and discounting it because it is merely *improbable*. The effect names the precise spot where low prior probability is mistaken for positive evidence of falsity, which clarifies why the error is self-sealing — each fresh protest raises the very prior it should challenge — and why escape cannot come from within the loop. Vindication has to arrive from a mechanism the original evaluation never consulted: an independent complaint, physical evidence, time. The practitioner who holds the concept can now ask not "is this claim plausible?" but "what would it take to verify or refute its referent, and has that been attempted?"

manages_complexity: Credibility evaluation under adverse priors throws up a recurring class of cases that look unrelated in their particulars — a psychiatric inpatient reporting assault by a named staff member, a detained person reporting institutional surveillance, a stalking victim describing a higher-status persecutor, a whistleblower describing a covert operation — and each, taken on its own, invites a bespoke judgment of whether the claimant is reliable. The Martha Mitchell effect compresses that variety into a single structural diagnosis: a *content-only evaluation routine* discounts a claim on its *prior probability* alone, and when the prior is wrong because the claim is true, the misclassification is not bad luck but systematic. From that one mechanism the qualitative trajectory of any such case is read off two tracked quantities, with no need to re-litigate the specifics: *how low the claim's prior probability is* under the evaluator's working model, and *how strong the claimant's pathologisation markers are* (prior psychiatric contact, isolation, intoxication, low status relative to the named perpetrator) — together these set how hard the evaluator leans on the prior instead of the referent, and so how likely a true report is to be filed as confabulation. The effect further makes the dynamics legible as a self-sealing loop: once the misclassification is entered, each protest is absorbed as fresh symptom and raises the very prior it should lower, which is why the trajectory cannot reverse from inside and why vindication must arrive from a mechanism the original evaluation never consulted — an independent complaint, physical evidence, corroborating witness, time. That branch structure converts an open-ended "is this claimant credible?" into a single checkable procedural question — *was the claim's external referent tested, or only its content re-examined?* — and pins the exact error to a definite spot: low prior probability mistaken for positive evidence of falsity. The compression here is more diagnostic than predictive — it does not forecast which claims are true, but it tells the evaluator precisely where a content-only routine will fail, what variables sharpen that failure, and which class of intervention (external verification, not further content review) can break a loop that cannot be broken from within.

abstract_reasoning:
The Martha Mitchell effect licenses a *procedural-gap* diagnostic that an evaluator runs on any low-prior claim made under adverse priors, and its moves are more about exposing where a routine will fail than forecasting which claims are true. The central inference reasons from the *structure of the evaluation* rather than the claim's content: if the only evidence weighed is how implausible the claim sounds and how the claimant presents, then a true low-prior report and a genuine confabulation are *identical from inside the consulting room*, so the analyst infers that "delusion," diagnosed by a content-only routine, is effectively *unfalsifiable* — and that the misclassification of a true low-prior report is not bad luck but systematic. The discriminating move the concept forces is the distinction between discounting a claim because it has been *shown false* and discounting it because it is merely *improbable*: the effect pins the exact error to the spot where low prior probability is mistaken for *positive evidence of falsity*, so the analyst reasons that improbability is not refutation and that a claim's low prior, by itself, licenses no conclusion about its referent.

The *loop-detection* move reads the dynamics as self-sealing and draws the consequence for intervention. Once the misclassification is entered, each subsequent protest is absorbed as fresh symptom and *raises the very prior it should lower*, so the analyst reasons that the trajectory cannot reverse from inside the loop — the claimant's strongest evidence (insistence) is converted into evidence against them — and therefore that vindication must arrive from a mechanism the original evaluation never consulted: an independent complaint, physical evidence, a corroborating witness, time. This is the *interventionist* inference: the corrective is not further review of the claim's *content* but a test of its *external referent*, and the analyst reasons that any escape route running through the content re-examination is closed by construction. The single checkable question the effect substitutes for "is this claim plausible?" is therefore "was the claim's external referent tested — was the named person present at the reported times, did a separate witness corroborate — or only its content re-examined?", converting an invisible structural trap into a procedural gap an auditor can check for.

The *risk-prediction* move, weaker and explicitly diagnostic rather than truth-forecasting, reads two quantities to gauge how exposed a given evaluation is: *how low the claim's prior probability is* under the evaluator's working model, and *how strong the claimant's pathologization markers are* (prior psychiatric contact, isolation, intoxication, low status relative to the named perpetrator). The analyst reasons that these jointly set how hard the evaluator leans on the prior instead of the referent, and so how likely a true report is to be filed as confabulation — predicting not which claims are true but which are most at risk of systematic misclassification, and therefore which most demand external verification. The *boundary-drawing* move scopes the construct to credibility evaluation under adverse priors with a content-only routine: the analyst reasons that the failure mode appears wherever an evaluator triages low-prior claims on plausibility and presentation without testing the referent, and that the same corrective — require positive evidence of falsity, not mere improbability, before discounting — is what breaks the loop in any such setting, even where it travels under other names (corroboration requirement, due process, independent fact-check).

knowledge_transfer:
Within clinical and forensic credibility evaluation the Martha Mitchell effect transfers as mechanism, because every setting it names is the *same* structure: an evaluator triaging a low-prior claim, under role pressure to use the prior efficiently, on a content-only routine that never tests the referent. It carries intact across the home cluster — psychiatric diagnosis (the named-staff assault report filed as persecution delusion), forensic evaluation (a detainee's report of institutional misconduct), trauma and coercive-control contexts (a stalking victim describing a higher-status persecutor), and whistleblowing/intelligence settings (a genuine covert-operation report). In all of them the same diagnostics apply: distinguish *shown false* from merely *improbable*; locate the exact error where low prior probability is mistaken for positive evidence of falsity; detect the self-sealing loop in which each protest raises the prior it should lower; and apply the one corrective the loop cannot supply from inside — test the external referent (was the named person present at the reported times, did a separate witness corroborate) rather than re-examine the content. The two risk parameters (how low the claim's prior, how strong the claimant's pathologization markers) gauge exposure the same way in a ward, a cell, or a field debrief. The vocabulary travels with it because the substrate — credibility evaluation under adverse priors — is constant beneath the changing scene.

Beyond the clinical cluster this entry is an unusually clean case of a *general pattern recurring across domains while its named machinery stays home*. The structural core — *under a sharp prior–likelihood asymmetry, posterior judgment collapses onto the prior and produces systematic error against true low-prior claims, and the error self-seals because the claimant's insistence is read as confirmation* — genuinely reappears in courts, journalism, regulatory review, and intelligence analysis. But it does not travel there as "the Martha Mitchell effect"; in each of those settings it has its own name and its own institutional furniture, and the corrective is the same move under a different label: *corroboration requirement* in law, *independent fact-check* in journalism, *due process* in adjudication. So the honest report is that what carries cross-domain is the general pattern — base-rate over-weighting collapsing the posterior onto the prior, plus the self-sealing-loop dynamics — and what stays home is the *clinical diagnostic apparatus* (the delusion category, the consulting-room presentation, the psychiatric vulnerability markers, the Watergate cultural anchor) that makes this the *Martha Mitchell* effect specifically. The cross-domain lesson should be carried by the parent constructs the effect instantiates, not by its name.

Those parents are explicit. The Bayesian core is `bayesian_updating` under prior misspecification, and the precise distortion is *base-rate over-weighting* — the directional inverse of `base_rate_neglect`, which under-weights the prior; the same-direction-as-conclusion re-reading of new evidence is `confirmation_bias`; and the systematic credibility discount applied to a claimant is `epistemic_injustice` (Fricker), with which the effect frequently compounds but is not identical (Martha Mitchell is prior-content-driven, not necessarily identity-driven). Carry those across domains; the named effect is one high-stakes, replication-uncontested diagnostic instance beneath them, illuminating because its loop dynamics and external-verification corrective are so sharply drawn — but its named cargo is clinical and stays clinical. The boundary to mark, then, is the one this section makes precise: within credibility evaluation the effect transfers literally as mechanism and remedy; beyond it the *pattern* recurs as co-instances under the Bayesian, confirmation-bias, and epistemic-injustice parents, each wearing its own domain's name for the same corrective. See *Structural Core vs. Domain Accent*.


Mass Assignment

one_liner: The web-application vulnerability in which a request-binding layer writes all client-supplied fields directly to a domain object with no allow-list, letting a caller set server-managed attributes like role or owner_id simply by naming them.

core_idea:
Mass assignment is the web-application security vulnerability in which a request-binding layer — the framework mechanism that maps incoming HTTP request parameters or JSON body fields to the attributes of an internal domain model object — accepts all key-value pairs supplied by the client and writes them directly to the corresponding object fields, without an allow-list governing which fields are externally settable. Fields the application intends to be controlled exclusively by authenticated server-side logic — `role`, `account_balance`, `is_admin`, `owner_id`, `subscription_tier`, `email_verified` — become silently settable by any caller who knows or guesses the field name and includes it in a request body.

The structural failure is in the binding layer itself, not in any particular field. The binding layer's job is to translate an untrusted external representation (HTTP form data, a JSON request body, a query string) into a trusted internal domain object; the vulnerability is that the layer performs this translation without distinguishing externally-settable fields from internally-managed fields. An attacker who knows or discovers a field name — by reading client-side HTML, by reading API documentation, by inspecting the framework's ORM schema in an error message, or by guessing from the application's domain model — can set that field in any request where the binding layer is invoked, with the full authority of the authenticated session. The 2012 GitHub mass-assignment vulnerability allowed a user to add their SSH public key to any organisation's repository by including an `owner` field in a key-creation request; the binding layer wrote it directly to the key object without validating that the authenticated user had authority over the named organisation.

The pattern recurs in every major web framework with automatic parameter binding: Rails' `update(params[:user])` without `permit`; Spring MVC's `@ModelAttribute` without an explicit DTO binding allowlist; ASP.NET MVC's model binding without `[Bind(Include=...)]`; Django REST Framework serializers without `read_only_fields`; Laravel's `fill()` without `$fillable`. Each framework has an allow-list mechanism — Rails' strong parameters (`permit`), Spring's allowlisted DTOs with `@Valid`, ASP.NET's `Bind` include list, Laravel's `$fillable` property — and the fix is identical in structure across all of them: enumerate the fields the binding layer is permitted to write; deny all others by default; never accept the request's own enumeration of what should be bound. The vulnerability is a canonical instance of the general security principle that default-deny at trust boundaries dominates default-allow: an allow-list closed to new fields is safe as the domain model grows; a deny-list open by default accumulates new exposure with every field added to the model until it is explicitly excluded.

scope_of_application:
Mass assignment lives within web-application security — wherever a framework's automatic request-binding or ORM-mapping layer translates client-supplied key-value pairs into internal domain-object fields without an allow-list; its reach is within that domain, the habitats being the specific frameworks whose binding layers exhibit the mechanic. The non-software gestures (an over-editable organizational form, an under-restricted amendment surface) are analogy; the genuinely portable part is the default-deny asymmetry, which belongs to the parent `access_control` / `authority` primes, not to "mass assignment" as named.

- **Ruby on Rails** — `update(params[:user])` without strong-parameters `permit`, the canonical instance; the fix is `params.require(:user).permit(...)` enumerating the writable fields.
- **Spring MVC** — `@ModelAttribute` binding without an explicit allowlisted DTO and `@Valid`, exposing model fields the request was never meant to set.
- **ASP.NET MVC** — model binding without a `[Bind(Include=...)]` list, writing whatever the request supplies into the bound object.
- **Django REST Framework** — serializers without `read_only_fields`, allowing client-supplied values to overwrite server-managed attributes.
- **Laravel** — Eloquent `fill()` / mass-assignment without a `$fillable` (or with an open `$guarded`) allow-list on the model.
- **OWASP / CWE-915 catalogue** — the standards home of the vulnerability (and the related CWE-915 "improperly controlled modification of dynamically-determined object attributes"), the cross-framework reference point for the binding-layer defect.

structural_signature:
**Sig role-phrases:**

- **the request-binding layer** — the framework mechanism that translates an untrusted external representation (HTTP form data, JSON body, query string) into a trusted internal domain object
- **the client-supplied key-value pairs** — the request's own enumeration of which fields to set, controllable by any caller who knows or guesses a field name
- **the internally-managed fields** — attributes (`role`, `account_balance`, `is_admin`, `owner_id`, `email_verified`) the application intends server-side logic to control exclusively
- **the missing allow-list** — the binding layer writing whatever it is handed, with no policy distinguishing externally-settable from internally-managed fields
- **the silent privilege write** — a server-managed field set with the full authority of the authenticated session, slipping past sound login and access control because neither inspects the field set of an already-authorized request
- **the three-concern partition** — authentication (who is acting), authorization (whether they may act on the resource at all), binding (which fields this action may set) — mass assignment attacking only the third
- **the default-deny asymmetry** — an allow-list closed to new fields stays safe as the model grows; a deny-list open by default accrues exposure with every added attribute
- **the allow-list remedy** — enumerate the writable fields and deny the rest, structurally identical across frameworks (Rails `permit`, Spring allowlisted DTOs, ASP.NET `Bind` include, Django REST `read_only_fields`, Laravel `$fillable`)

what_it_is_not:
- **Not a problem in any particular field.** The defect is not in `role`, `is_admin`, or `owner_id`; it is in a binding layer that writes whatever it is handed. The 2012 GitHub exposure lived not in the `owner` field but in the translation step that bound it unfiltered — so the fix is at the binding site, not an audit of sensitive attributes.
- **Not an authentication failure.** Authentication asks *who* is acting, and mass assignment slips straight past sound login: the attacker is a fully authenticated session. Hardening who-you-are leaves the field-set of the request untouched.
- **Not an authorization failure.** Authorization asks whether the actor may act on this *resource* at all; mass assignment attacks a third axis — which *fields* an already-authorized action may set. Sound access control never examines the field set of a permitted request, which is exactly why this class evades it.
- **Not a per-field validation task.** Treated as fields, the remedy is open-ended and never finished — every attribute the next feature adds reopens it. Treated as a binding problem, it is a closed task: enumerate the writable fields, deny the rest by default, and stop trusting the request's own enumeration of what to bind.
- **Not fixable with a deny-list.** A deny-list open by default accrues fresh exposure with every attribute added to the model until someone remembers to exclude it; only an allow-list closed to new fields stays safe as the domain model grows. The vulnerability is the structural instance of why default-deny dominates default-allow at a trust boundary.
- **Not injection.** Injection crosses untrusted text into a control channel that an interpreter executes; mass assignment writes well-formed field values into object attributes the client was never meant to control. No interpreter is tricked — the binding layer does precisely what it was told, having been told too much.

clarity:
Naming mass assignment converts a vague reviewer worry — "the user can change things they shouldn't" — into a single, located defect: the binding layer maps request fields to object fields with no allow-list. That relocation is the concept's main service, because it changes the *shape* of the fix. Treated as a field problem, the remedy is open-ended and never finished — audit `role`, audit `is_admin`, audit `owner_id`, and audit every attribute the next feature adds. Treated as a binding problem, the remedy is a closed, finite task: enumerate the fields the layer may write, deny the rest by default, and stop trusting the request's own enumeration of what to bind. The vulnerability in the 2012 GitHub case was never *in* the `owner` field; it was in a binding layer that wrote whatever it was handed — and the label is what makes that the thing under review.

Its second clarifying service is to pull apart three concerns defenders routinely fuse, so the right defence is aimed at the right gap. *Authentication* asks who is acting; *authorization* asks whether they may act on this resource at all; *binding* asks which fields this action is permitted to set. Mass assignment attacks the third alone — which is exactly why it slips past systems with sound login and sound access control, since neither examines the field set of an already-authorized request. Holding the trio distinct also sharpens the design question from the reactive "which fields are dangerous?" to the structural "is this trust boundary default-deny or default-allow?" — surfacing the asymmetry that an allow-list closed to new fields stays safe as the domain model grows, whereas a deny-list open by default silently accrues fresh exposure with every attribute added until someone remembers to exclude it.

manages_complexity: Taken at the level of fields, the exposure this vulnerability creates is unbounded and ever-growing: `role`, `account_balance`, `is_admin`, `owner_id`, `subscription_tier`, `email_verified`, and every server-managed attribute the next feature adds is a thing a client might set, so an audit that chases dangerous fields is open-ended by construction — it is never finished, and each new column in the domain model reopens it. The mass-assignment concept collapses that field-by-field sprawl by relocating the defect from any particular attribute to one architectural site, the request-binding layer, and asserting that the field was never the problem: the GitHub `owner` exposure lived not in the `owner` field but in a binding layer that wrote whatever it was handed. Once the unit of analysis is the binding site rather than the attribute, what the analyst tracks shrinks from "which of our growing list of fields are sensitive" to a single closed question asked at each place the request-to-object translation happens: does an allow-list govern which fields this layer may write, or does it accept the request's own enumeration of what to bind? From that one property the qualitative outcome reads off directly and stays valid as the model grows — an allow-list closed to new fields is safe no matter how many attributes are added, because new fields are denied by default; a deny-list (or no list) open by default silently accrues fresh exposure with every attribute until someone remembers to exclude it. This is the asymmetry the concept makes primary: default-deny at the trust boundary dominates default-allow, so the remedy becomes a finite, structurally identical task across every framework — Rails `permit`, Spring allowlisted DTOs, ASP.NET `Bind` include lists, Django REST `read_only_fields`, Laravel `$fillable` — namely, enumerate the writable fields and deny the rest, rather than an endless per-field hunt. The branch structure compresses the diagnosis the same way and matters because it routes the defence to the right gap: the three-concern partition the concept draws — authentication (who is acting), authorization (whether they may act on this resource at all), binding (which fields this action may set) — sorts mass assignment cleanly onto the third axis alone, which is exactly why it slips past sound login and sound access control, neither of which inspects the field set of an already-authorized request. Holding that fork tells the defender that hardening authentication or authorization will not touch this class, and that the single structural question to ask of the boundary is whether it is default-deny or default-allow. So the move is from an unbounded, regrowing audit of sensitive fields to one allow-list check per binding site and a three-way partition that says which defence applies — a high-dimensional "which fields are dangerous" problem reduced to a closed structural property of the binding layer.

abstract_reasoning:
Mass assignment licenses reasoning moves that all relocate the defect from any particular field to one architectural site — the request-binding layer — and ask a single closed question there. The most distinctive is a *diagnostic relocation* move that overturns a field-level reading. A reviewer worried that "the user can change things they shouldn't" instinctively chases dangerous attributes — audit `role`, `is_admin`, `owner_id`, and whatever the next feature adds; the concept asserts the field was never the problem, that the GitHub `owner` exposure lived not in the `owner` field but in a binding layer that wrote whatever it was handed. Reasoning from that, the analyst moves the unit of analysis from the attribute to the binding site and infers that the vulnerability is the layer translating an untrusted external representation into a trusted internal object without distinguishing externally-settable from internally-managed fields. This changes the *shape* of the problem from an open-ended field hunt into a property of one location.

The governing move asks, at each place the request-to-object translation happens, a single closed question: does an allow-list govern which fields this layer may write, or does it accept the request's own enumeration of what to bind? Reasoning from that one property, the analyst reads exposure off the binding site directly and — critically — predicts how it behaves *as the domain model grows*. The reasoning surfaces an asymmetry the concept makes primary: an allow-list closed to new fields stays safe no matter how many attributes are added, because new fields are denied by default, whereas a deny-list (or no list) open by default silently accrues fresh exposure with every attribute until someone remembers to exclude it. So the analyst reasons over time, not just over the current schema, concluding that default-deny at the trust boundary dominates default-allow because only the closed policy is stable under model growth.

A *boundary-drawing* move partitions three concerns defenders routinely fuse, and it is load-bearing because it routes the defence to the right gap. The analyst separates authentication (who is acting), authorization (whether they may act on this resource at all), and binding (which fields this action is permitted to set), and reasons that mass assignment attacks the third axis alone. From that partition follows a sharp prediction: the class slips past systems with sound login and sound access control, because neither examines the field set of an already-authorized request — so hardening authentication or authorization is predicted to leave this vulnerability untouched, and the single structural question to ask of the boundary is whether it is default-deny or default-allow. This fork tells the analyst which defence applies before any code is changed.

The *interventionist* move reasons forward from the located binding defect to a remedy that is finite and structurally identical across frameworks. Because the fix is to make the binding layer default-deny, the analyst enumerates the fields the layer is permitted to write, denies all others, and never accepts the request's own enumeration of what to bind — instantiated as Rails `permit`, Spring allowlisted DTOs with `@Valid`, ASP.NET `Bind` include lists, Django REST `read_only_fields`, or Laravel `$fillable`, all the same move. The analyst predicts that this converts an open-ended, regrowing audit into a closed task that remains correct as the model grows, precisely because the allow-list denies new fields by default. The recurring habit the concept installs is to inspect each binding site for whether an allow-list governs it, to reason about its safety under future field additions rather than only its current fields, and to sort any "user changed something they shouldn't" report onto the authentication/authorization/binding partition before selecting a fix.

knowledge_transfer:
Within software the concept transfers *as mechanism* and the transfer is unusually clean, because the fix is structurally identical wherever automatic request-to-object binding exists. The same diagnosis (relocate the defect from any sensitive field to the binding site), the same closed question (does an allow-list govern which fields this layer may write, or does it accept the request's own enumeration?), the same stability argument (an allow-list closed to new fields stays safe as the model grows; a deny-list open by default accrues exposure with every added attribute), and the same three-concern partition (authentication / authorization / binding, with mass assignment attacking only the third) carry without translation across every major framework. They instantiate as Rails strong parameters (`permit`), Spring allowlisted DTOs with `@Valid`, ASP.NET `[Bind(Include=...)]`, Django REST `read_only_fields`, and Laravel `$fillable` — the same move in different syntax. The vocabulary does not strain across these because they are one substrate: web frameworks with parameter-binding or ORM mapping layers. (The apparent sector extensions — public-administration form intake, healthcare record updates, finance account modification — are themselves software systems sharing the identical binding-layer mechanic, not a move off-substrate.)

Beyond software the honest account is twofold. The *named* concept — "mass assignment," with its binding layer, allow-list mechanisms, ORM field-mapping, and OWASP/CWE-915 identity — does not transfer non-metaphorically: the non-software gestures one might reach for (an organizational form with too many editable fields, a legal document with an under-restricted amendment surface) borrow the *shape* and rename the components but drop the machinery (there is no request-binding layer, no per-framework `permit`/`$fillable`, no field-set of an already-authorized request to inspect), so those are analogy, case (A), and should be marked as such. But the *principle the vulnerability instantiates* is a genuine *shared abstract mechanism* — case (B): default-deny at a trust boundary dominates default-allow because only the closed policy is stable when new items can be added faster than a deny-list is updated (whitelist-closed beats blacklist-open). That principle really does recur across substrates — physical and administrative access control, capability systems, firewall and permission design, any boundary admitting items from a less-trusted into a more-trusted context — and it carries a real, transferable lesson. The honest move, then, is that the cross-domain reach belongs to the parent primes the concept instantiates — `access_control`, `authority`, and the general trust-boundary-crossing pattern — which already carry the default-deny asymmetry substrate-neutrally; "mass assignment" is the specific web-framework instance of those primes and does not, as named, float free of the binding-layer substrate (see *Structural Core vs. Domain Accent*).


Mass Wasting

one_liner: Predict whether a slope fails by comparing resisting to driving force on a specified shear surface as a single factor-of-safety ratio, stable above unity and failing below it, with gravity as the sole transport agent.

core_idea:
Mass wasting is the geomorphic process by which rock, regolith, or soil moves downslope under the direct pull of gravity once the driving forces — primarily the downslope component of gravitational stress on a candidate failure surface — exceed the resisting forces, which are shear strength composed of cohesion, internal friction, and pore-water pressure effects on effective normal stress. It is distinguished from fluvial, glacial, and aeolian transport by gravity acting as the sole transport agent, with no flowing medium to carry the material.

The central analytical tool is the factor of safety: the ratio of the total resisting force to the total driving force on a specified failure surface. When the factor of safety exceeds unity, the slope is stable; when it drops below unity, the slope fails. Failure surfaces are identified by material discontinuities, weak layers, bedding planes, or saturated horizons. Triggers — heavy rainfall, earthquake shaking, toe undercutting by a river or wave, progressive weathering, loss of vegetation — reduce the factor of safety, sometimes abruptly and sometimes through slow preconditioning, until the threshold is crossed.

Mass-wasting events are classified by their movement mechanism and material: falls (free-fall detachment of blocks), topples (overturning), slides (translation or rotation along a discrete surface), flows (internal deformation throughout the moving mass, typically when water-saturated), spreads (lateral extension of the surface layer), and creep (extremely slow, continuous plastic deformation). Hazard and runout distance are governed by the volume of displaced material, its water content, and the slope geometry. Mass wasting operates at scales from individual rock falls on a cliff face to catastrophic submarine landslides such as the Storegga slide (ca. 8,150 years ago, generating a major North Atlantic tsunami), and it is a principal mechanism of hillslope sediment delivery to channel networks, making it foundational to landscape-evolution modelling, hazard mapping, and geotechnical slope design.

scope_of_application:
Mass wasting lives across the geomorphology and geotechnical-engineering subfields of the earth sciences — wherever gravity is the sole transport agent failing a shear surface on a slope; its reach is within that one substrate of gravity-loaded slope geomechanics. The "debt cascade" / "organisational collapse" extensions borrow the runout picture but drop the Mohr-Coulomb criterion, so they are carried by `stress_rupture` / `tipping_points` / `gradual_deterioration` / `cascade`, not the factor-of-safety equation.

- **Landslides, slumps, and rotational failures** — the classic slope-failure events with identifiable shear surfaces, the home case for factor-of-safety analysis.
- **Rockfalls and topples** — discrete-block detachment from cliff faces under freeze-thaw, root wedging, or undercutting.
- **Debris flows and lahars** — water-saturated mass movement common after wildfire (Santa Barbara) or eruption (Nevado del Ruiz), where pore pressure and rheology govern long, fast runout.
- **Soil creep** — extremely slow plastic deformation of regolith downslope, the imperceptible end of the movement-mechanism range.
- **Submarine slope failure** — continental-slope collapses generating tsunamis (the Storegga slide) and threatening seafloor cables and infrastructure.
- **Mining slope stability** — open-pit bench-scale and overall slope design and monitoring against gravity-driven failure.
- **Permafrost geomorphology** — thaw-induced solifluction and slope failure on warming Arctic slopes, an Arctic-amplification-driven mass-wasting regime.
- **Landscape-evolution modeling and hazard mapping** — mass wasting as a principal mechanism of hillslope sediment delivery to channel networks, foundational to denudation budgets and hazard assessment.

structural_signature:
**Sig role-phrases:**

- **the gravity-loaded slope** — rock, regolith, or soil on an incline, with gravity as the sole transport agent (no flowing medium)
- **the candidate failure surface** — a specific shear surface defined by material discontinuities, weak layers, bedding planes, or saturated horizons
- **the driving force** — the downslope component of gravitational stress on that surface
- **the resisting force** — shear strength from cohesion, internal friction, and the effective normal stress that pore-water pressure modulates
- **the factor of safety** — the scalar ratio of resisting to driving force on the specified surface: stable above unity, failing below it
- **the precondition vs. trigger** — slow preconditioning (weathering, creep, rising pore pressure, vegetation loss) walking the ratio toward unity, then an abrupt trigger (rainfall, earthquake, toe undercutting) crossing it
- **the movement mechanism** — the Varnes grid (fall, topple, slide, flow, spread, creep crossed with material) classifying how the mass moves
- **the runout** — distance, velocity, and impact of the moving mass, set by volume, water content, and slope geometry

what_it_is_not:
- **Not the trigger that immediately preceded it.** The rainfall, earthquake, or undercutting that set off a slide is the proximate event, not necessarily the cause: the factor of safety is often walked toward unity over millennia by slow preconditioning (weathering, creep, rising pore pressure, vegetation loss), so the real fragility may have been built long before the trigger arrived. Mass wasting separates the long preparation of a slope from the proximate perturbation, and treats the two as distinct.
- **Not always catastrophic or sudden.** A landslide is the conspicuous case, but mass wasting spans the full range down to soil *creep* — imperceptibly slow, continuous plastic deformation that moves regolith downslope without any discrete failure event. The defining feature is gravity-driven downslope movement, not violence or speed; an inch-a-year creeping hillside is mass wasting as surely as a debris flow.
- **Not transport by a flowing medium.** What distinguishes mass wasting from fluvial, glacial, and aeolian transport is that *gravity is the sole transport agent* — there is no river, ice sheet, or wind carrying the material. Even a water-saturated debris flow is not water *carrying* sediment the way a stream does; the water lowers the slope's strength (via pore pressure) and changes the flow rheology, but the mass moves because gravity pulls it, not because a medium conveys it.
- **Not a property of "the slope" in general.** The factor of safety is defined on a *specified* candidate failure surface, not on the hillside as a whole. The same slope can be stable against one surface and failing against another (a weak bedding plane, a saturated horizon), so "will this slope fail?" is only well-posed once a particular surface whose strength can be characterised is named.
- **Not safe just because the geometry and material look sound.** A slope of modest angle in competent material can still fail, because pore-water pressure reduces the effective normal stress that friction depends on. Water is frequently the decisive variable on a slope that otherwise appears stable, so apparent strength of the rock and a gentle profile do not by themselves guarantee a factor of safety above unity.

clarity:
Treating mass wasting as a process in its own right makes the hillslope's sediment budget legible by partitioning it: the material gravity moves directly downslope is separated from what rivers, glaciers, and wind carry, and that partition is what lets a geomorphologist attribute an erosion rate, a sediment delivery to a channel, or a landscape's evolution to the correct agent rather than lumping all denudation together. The Varnes-style classification then sharpens the picture further, resisting the conflation of phenomena that look superficially alike — a debris flow deforming throughout its mass is not a slide translating along a discrete surface, and a rockfall is not a slump — because movement mechanism and material content govern runout, velocity, and hazard differently. The single most clarifying instrument the concept supplies is the factor of safety, which reduces "will this slope fail?" to a statable ratio of resisting to driving force on a *specified* surface, and in doing so locates the question correctly: not at the slope in general but at a particular candidate failure surface whose strength can be characterised.

That framing dissolves a confusion endemic to slope hazard — the conflation of the *trigger* with the *cause*. Because the factor of safety can be driven toward unity either by slow preconditioning (weathering, creep, rising pore-water pressure, vegetation loss) or by an abrupt perturbation (an earthquake, a river undercutting the toe), the concept separates the long preparation of a slope from the proximate event that tips it over, and tells the analyst that the rainfall or the shaking which immediately preceded a failure is often not where the real fragility was built. The sharper questions this licenses are diagnostic and interventionist at once: which surface is approaching failure, what is driving its factor of safety down, and which term in the resisting-versus-driving balance — drainage to cut pore pressure, reinforcement to add strength, regrading to reduce the driving stress — can be moved to raise it. It also exposes a counterintuitive lever: because pore-water pressure reduces the effective normal stress that friction depends on, water is frequently the decisive variable on a slope whose geometry and material look otherwise sound.

manages_complexity: Downslope movement presents the geomorphologist with enormous heterogeneity — rockfalls and topples on a cliff, rotational slumps, translational slides, water-saturated debris flows and lahars, imperceptible creep, catastrophic submarine slope failures — spanning materials, rates, triggers, and scales from a single block to the Storegga slide. Mass wasting compresses the stability question across that whole range to one scalar, the factor of safety: the ratio of resisting to driving force on a *specified* failure surface, stable above unity and failing below it. Whatever the material or scale, the analyst asks the same thing of a candidate surface and reads off the binary outcome, rather than re-deriving the mechanics of each slope from scratch. The driving side reduces to the downslope component of gravitational stress; the resisting side to shear strength (cohesion, internal friction, and the effective normal stress that pore-water pressure modulates) — so a slope's whole stability state is carried by a handful of parameters, and the decisive lever is identified by inspection of which term in that ratio can be moved (drainage to cut pore pressure, reinforcement to add strength, regrading to reduce the driving stress). Time enters the same compressed picture: the factor of safety can be walked toward unity by slow preconditioning or dropped across it by an abrupt trigger, so the analyst separates the long preparation of a slope from the proximate event without tracking the full history. And the diversity of outcomes is organized by a second small structure — the Varnes grid of movement mechanism (fall, topple, slide, flow, spread, creep) crossed with material — from which runout distance, velocity, and hazard follow once volume, water content, and slope geometry are known. A field of irreducibly varied gravitational events thus collapses to: specify a surface, compute one ratio, classify the mechanism, and read off both whether the slope fails and how far the failure travels.

abstract_reasoning:
Mass wasting licenses a slope-stability reasoning kit organized around one scalar — the factor of safety — and one typology — the Varnes movement-mechanism grid.

*Predictive — read stability off the factor of safety.* The defining inference runs FROM the ratio of resisting to driving force on a *specified* failure surface TO a binary verdict: factor of safety above unity, the slope is stable; below unity, it fails. Reasoning is anchored to a particular candidate surface, not the slope in general, so the analyst computes the downslope component of gravitational stress (driving) against shear strength — cohesion, internal friction, and the effective normal stress that pore-water pressure modulates (resisting) — and reads the outcome from a handful of parameters rather than solving the slope's full mechanics.

*Diagnostic — locate the decisive term, and water as the usual culprit.* Given a slope approaching failure, the move infers *which* term is driving the factor of safety down. Because pore-water pressure reduces the effective normal stress that friction depends on, the move flags water as frequently the decisive variable even on a slope whose geometry and material look sound — so reasoning runs FROM a stability drop TO rising pore pressure as the leading hypothesis, with geometry and material strength as the alternatives to rule out. Identifying the controlling term is what makes the diagnosis actionable.

*Interventionist — move the term that raises the ratio.* The factor-of-safety structure names the levers directly: reason FROM the term one can change TO the predicted effect on stability — drainage to cut pore-water pressure (raising effective normal stress and thus friction), reinforcement to add shear strength, regrading to reduce the driving stress. Each intervention is a prediction that the factor of safety rises by a calculable amount, so the analyst chooses the lever by which term in the ratio is both decisive and movable.

*Diagnostic — separate trigger from precondition.* The concept dissolves the conflation of the proximate event with the real fragility. Because the factor of safety can be walked toward unity by slow preconditioning (weathering, creep, rising pore pressure, vegetation loss) or dropped across it by an abrupt perturbation (an earthquake, toe undercutting), the move reasons FROM a failure backward to *both* a long preparation and a proximate trigger — and warns that the rainfall or shaking immediately preceding a slide is often not where the fragility was built. The order-of-events inference is explicit: preconditioning lowers the margin over time, then a trigger crosses the threshold.

*Predictive — classify the mechanism and forecast runout.* The Varnes grid (fall, topple, slide, flow, spread, creep crossed with material) lets the analyst infer movement style and, from it, the downstream hazard. Reason FROM the mechanism plus volume, water content, and slope geometry TO runout distance, velocity, and impact — a water-saturated flow travelling far and fast where a translational slide along a discrete surface stops sooner — so the failure's consequences, not just its occurrence, are predicted from a small set of quantities.

knowledge_transfer:
Within geomorphology and geotechnical engineering, mass wasting transfers *as mechanism*, intact, across the full range of slope-failure settings because the underlying geomechanics is shared. The factor-of-safety scalar, the Mohr-Coulomb resisting-versus-driving decomposition, the pore-water-pressure analysis (Bishop, Janbu, Spencer methods of slices), the trigger-versus-precondition separation, the Varnes movement-mechanism grid, and the runout-from-volume-and-friction prediction all carry without translation from terrestrial landslides, slumps, and rockfalls to debris flows and lahars (post-wildfire Santa Barbara, post-eruption Nevado del Ruiz), to soil creep, to submarine continental-slope failures (the Storegga slide and seafloor-cable hazards), to open-pit mining slope design, and to permafrost solifluction on warming Arctic slopes. The monitoring toolkit (inclinometers, rainfall thresholds, InSAR) and the intervention vocabulary (drainage to cut pore pressure, soil nails and retaining walls to add strength, regrading to cut driving stress, deflection berms to manage runout) port across all of these because each is the same process — gravity-driven failure of a shear surface — with only the material, rate, and trigger refilled. The variation across these cases is large, but the substrate is one, which is exactly why mass wasting is a domain-specific abstraction: its predictive force comes from geomechanics that does not survive extraction from gravity-loaded slopes.

Beyond that substrate the term is borrowed widely but the transfer is *analogy*: "organisational collapse," "debt cascades," "debt avalanche," "infrastructure failure," "collapse cascade" lift the kinetic surface idea — something gives way and material runs downhill — while discarding the load-bearing structure: there is no Mohr-Coulomb failure criterion, no pore-water pressure, no shear surface to identify, no slope geometry. These usages keep the *picture* of threshold-crossing-then-runout but rename the components (slope → balance sheet/organisation, pore pressure → leverage/stress, runout → contagion), so the resemblance is illuminating but pattern-by-resemblance, and should be marked as metaphor rather than the mechanism traveling.

What genuinely carries cross-domain is the substrate-independent residue, and the honest move (case B) is to let the parent primes carry it. Strip the geomechanics and what remains is "a system constrained against a driving force fails rapidly when the constraint is overcome, and the failure propagates along internal paths until potential energy is dissipated" — and that decomposes cleanly into already-catalogued patterns: load-exceeds-capacity rupture is `stress_rupture`, the threshold crossing into a new regime is `tipping_points` (and `threshold`), the slow approach to the threshold via weathering, creep, and rising pore pressure is `gradual_deterioration`, and the propagation of failure downslope or down-network is `cascade`. When the lesson "slow accumulation of fragility, a trigger crossing the threshold, then rapid propagation" is needed in finance or organizational sociology, it is *these* primes that recur as co-instances and should carry it — the debt-avalanche analogy is delivered by `gradual_deterioration` + `tipping_points` + `cascade`, not by importing the factor-of-safety equation. Mass wasting's own contribution — the gravity-driven specifics: the Mohr-Coulomb surface, pore-pressure-as-decisive-lever, the Varnes runout dynamics — is the home-bound cargo that stays in geomorphology. The general constraint-overcome-then-runout pattern travels via `stress_rupture`, `tipping_points`, `gradual_deterioration`, and `cascade`; the named process stays on the slope, the boundary *Structural Core vs. Domain Accent* makes precise below.


Matching

slug: matching

one_liner: Cast a pairing problem as a largest (or minimum-cost, or perfect) set of pairwise vertex-disjoint edges on an explicit graph, then split on bipartiteness to select the theorems and polynomial-time algorithm that solve it.

origin_domain: mathematics

core_idea:
A matching in a graph $G = (V, E)$ is a subset $M \subseteq E$ of edges such that no two edges in $M$ share a vertex — every vertex is incident to at most one edge of $M$. The vertices touched by edges of $M$ are *matched*; the remainder are *unmatched*. A *perfect matching* matches every vertex; a *maximum matching* has the largest possible number of edges; a *maximal matching* admits no further edge without violating the disjointness condition.

The structural richness of the concept lies in the theorem theory and algorithm theory it supports. Hall's marriage theorem (1935) characterizes when a bipartite graph has a perfect matching: a bipartite graph with sides $A$ and $B$ has a perfect matching saturating $A$ if and only if every subset $S \subseteq A$ has at least $|S|$ neighbors in $B$ — the marriage condition. König's theorem identifies the maximum matching size in bipartite graphs with the minimum vertex cover, connecting matching to covering via LP duality. Tutte's theorem (1947) characterizes perfect matching existence in general graphs via odd components. Edmonds's blossom algorithm (1965) finds maximum matchings in general graphs in polynomial time $O(|V|^3)$, a foundational result in combinatorial optimization partly because the algorithm introduced the technique of shrinking odd cycles (*blossoms*) to handle the non-bipartite case. For bipartite weighted matching — assigning a cost $w(e)$ to each edge and minimizing total cost over perfect matchings — the Hungarian algorithm (Kuhn, 1955; Munkres, 1957) solves the problem in $O(|V|^3)$ and is reducible to maximum-flow via standard constructions. Maximum matching on planar graphs is solvable in $O(n^{3/2})$ via the dual graph. The matching polytope — the convex hull of characteristic vectors of matchings in $G$ — has a complete half-integral linear-programming description for bipartite graphs (constraints per edge plus degree constraints per vertex), and a more complex description involving odd-set inequalities for general graphs, placing matching at the center of the polyhedral combinatorics literature.

scope_of_application:
Because matching is a mathematical construct — a largest pairwise vertex-disjoint edge set on an explicit graph — not a causal mechanism, it applies literally wherever a problem genuinely reduces to vertex-disjoint edges on a stated graph, across mathematics and computer science. The habitats below are genuine uses of the identical machinery; where "matching" is invoked cross-domain with preferences and mutual fit it usually means the *different* object `two_sided_matching` (stability, not cardinality), and one-sided cases read as `allocation` / `optimization`.

- **Graph theory and combinatorics** — the home theory: maximum, maximal, and perfect matchings, the existence characterizations (Hall's marriage theorem, Tutte's odd-component condition, König's matching = minimum-vertex-cover identity), and the matching polytope with its odd-set inequalities.
- **Combinatorial optimization** — the assignment problem: minimum-cost weighted bipartite matching solved by the Hungarian algorithm, and Edmonds's blossom algorithm finding maximum matchings in general graphs in polynomial time.
- **Operations research and scheduling** — constructed-graph reductions: assigning workers to jobs, exams to slots, machines to tasks, and timetabling, each cast as matching on a built graph.
- **Network flow** — the max-flow connection: bipartite matching reducible to maximum flow, sitting at the polynomial-solvable frontier whose extensions (3-D matching) sharpen the NP-hard boundary.
- **Algorithm theory** — the canonical exposition: matching as the standard vehicle for augmenting-path arguments and LP-relaxation / polyhedral-combinatorics reasoning.

structural_signature:
**Sig role-phrases:**

- **the graph** — $G=(V,E)$ supplying vertices (entities) and edges (eligible pairings) that the model must state explicitly
- **the matching $M$** — a subset $M\subseteq E$ of pairwise vertex-disjoint edges, every vertex incident to at most one edge of $M$
- **the matched and unmatched vertices** — those touched by an edge of $M$ versus the remainder
- **the variant target** — which objective is sought: maximum (most edges), minimum-cost weighted (the assignment problem), maximal (merely unextendable), or perfect (every vertex covered)
- **the bipartite/non-bipartite split** — the one decisive parameter selecting the whole downstream toolkit (clean augmenting-path/Hungarian/half-integral-polytope machinery versus blossom shrinking and odd-set inequalities)
- **the alternating and augmenting paths** — the engine of enlargement: a path alternating matched/unmatched edges, augmenting when it starts and ends unmatched, used to grow $M$
- **the existence certificates** — the engineered structural verdicts with witnesses: Hall's marriage condition (bipartite), Tutte's odd-component condition (general), König's matching = minimum-vertex-cover identity
- **the polynomial-time algorithms** — augmenting paths, the Hungarian method (weighted bipartite), Edmonds's blossom algorithm (general), reduction to max-flow
- **the matching polytope** — the convex hull of matching vectors, half-integral for bipartite graphs and requiring odd-set inequalities in general
- **the expressiveness limitation** — the boundary: plain matching captures only largest pairwise-disjoint edge sets, so preferences inducing stability, three-way groupings, or per-vertex capacities require generalizations (stable matching, hypergraph matching, $b$-matching)

what_it_is_not:
- **Not stable matching.** Graph matching maximizes cardinality or total weight (a set of pairwise vertex-disjoint edges); *stable* matching is a different mathematical object whose target is stability under each side's preferences — no two unmatched parties prefer each other to their assignments. The two answer different questions (most/cheapest pairs versus no blocking pair), have different algorithms (blossom/Hungarian versus Gale–Shapley), and should not be conflated.
- **Not "maximal equals maximum."** A *maximal* matching merely admits no further edge without violating disjointness; a *maximum* matching has the largest possible number of edges. A maximal matching can be far smaller than a maximum one — the vocabulary keeps these apart precisely because the easy greedy target (maximal) is not the optimization target (maximum).
- **Not any pairing problem.** Matching requires an *explicit graph* and captures only largest pairwise-vertex-disjoint edge sets. Problems with preferences inducing stability, three-way groupings, or capacities beyond one-per-vertex exceed what plain matching expresses and require generalizations (stable matching, hypergraph matching, $b$-matching); forcing them into the basic model misrepresents them.
- **Not indifferent to bipartiteness.** Whether the graph is bipartite is the one decisive parameter: it separates the clean augmenting-path / Hungarian / half-integral-polytope machinery from the odd-cycle (blossom) algorithm and odd-set inequalities the general case demands. Treating all matching problems as one ignores the line that selects the entire downstream toolkit.
- **Not a substrate-independent prime.** Matching is mathematical *machinery* — an edge set with a disjointness constraint plus its theorems (Hall, König, Tutte) and algorithms — used to model patterns, not a portable structural force. Cross-domain, "matching" usually means `two_sided_matching` (when preferences and mutual fit matter) or `allocation`/`optimization` (one-sided), which carry the transferable content; the Hall/König/Tutte/blossom/polytope cargo does not generalize.

clarity:
Naming the matching pins down precisely what is being computed and forces the looseness out of a pairing problem. By demanding an explicit graph — vertices and the edges that count as eligible pairs — the concept makes a modeler state, before any optimization, exactly which pairings are admissible and what is being maximized; "find a good set of pairs" becomes the sharp question "find a largest set of pairwise vertex-disjoint edges." It also separates objects that informal usage blurs: an unweighted *maximum* matching (most edges), a weighted *minimum-cost* matching (the assignment problem), a *maximal* matching (merely unextendable), and *perfect* matching (every vertex covered) are distinct targets with distinct algorithms, and the vocabulary keeps them from being confused for one another.

The deepest clarity it brings is turning the existence question into a structural one with a checkable answer. Rather than searching for a perfect matching and giving up when none appears, the practitioner asks *what obstructs one?* — and the answer is named: in a bipartite graph, a subset of one side with too few neighbors (Hall's marriage condition); in a general graph, too many odd components (Tutte). König's theorem sharpens this further, identifying the maximum matching size with the minimum vertex cover, so a failure to enlarge a matching has a *certificate* — a small cover, a deficient set — rather than being a mere negative result. The same framing exposes the decisive dividing line of the field: whether the graph is bipartite or not, since that is what separates the straightforward augmenting-path methods and the half-integral matching polytope from the odd-cycle (blossom) machinery and odd-set inequalities the general case demands. Knowing a problem *is* a matching, and which side of that line it falls on, tells the analyst immediately which theorems characterize it and which algorithm solves it.

manages_complexity:
A vast and superficially heterogeneous collection of problems — assigning workers to jobs, scheduling exams to slots, pairing items under eligibility, covering a structure with disjoint pairs — would, taken at face value, each demand its own formulation, its own existence argument, and its own solution method. Matching compresses that sprawl by recognizing all of them as one combinatorial object: a set of pairwise vertex-disjoint edges on an explicitly stated graph. The moment a problem is cast as a matching, the modeler stops improvising and tracks a few fixed parameters — what the vertices are, which edges count as eligible pairs, whether edges carry weights, and what is being maximized — off which the entire apparatus of the field becomes available without rederivation. The variant names do the rest of the sorting: maximum (most edges), minimum-cost weighted (the assignment problem), maximal (merely unextendable), perfect (every vertex covered) are kept distinct, each routed to its own characterization and algorithm. A question that looked like a bespoke pairing puzzle collapses to identifying which matching variant it is, after which the qualitative facts — does a solution exist, how large, by what algorithm, at what cost — are read off known theory rather than worked out from scratch.

The compression's branch structure is organized by one decisive parameter: is the graph bipartite or not? That single bit selects the whole downstream toolkit. On the bipartite side sit the augmenting-path methods, the Hungarian algorithm for the weighted case, the reduction to maximum flow, König's matching–vertex-cover identity, and the clean half-integral matching polytope; on the general side, the problem demands Edmonds's blossom machinery for shrinking odd cycles and the odd-set inequalities that complicate the polytope. Knowing a problem is a matching and which side of that line it falls on tells the analyst immediately which theorems govern and which polynomial-time algorithm applies. The existence question is compressed the same way, from an open-ended search into a checkable structural verdict with a certificate: a bipartite perfect matching exists exactly when Hall's marriage condition holds (no subset of one side has too few neighbors), a general one exactly when Tutte's odd-component condition holds, and a matching that cannot be enlarged comes with a witnessing minimum vertex cover or deficient set rather than a bare failure. So instead of re-deriving solvability and method for every pairing problem it meets, the analyst reads the outcome off three things — the matching variant, the bipartite/non-bipartite split, and the relevant obstruction certificate — turning a high-dimensional family of case-specific problems into a low-dimensional lookup with predictable branches.

abstract_reasoning: The matching concept licenses a set of moves on any pairing problem, all routed through casting it as pairwise vertex-disjoint edges on an explicit graph and reading the field's apparatus off a few parameters. *Boundary-drawing (the founding move) — force the admissible pairs and the objective into the open*: the foundational move is to refuse the loose brief "find a good set of pairs" and to demand an explicit graph — what the vertices are, which edges count as eligible pairs — and a stated objective, so that the problem becomes "find a largest (or minimum-cost, or perfect) set of pairwise vertex-disjoint edges." The analyst reasons from "assign workers to jobs under eligibility" to "vertices = workers and jobs, edges = eligible assignments, maximize the matched count," so the move is to fix the model precisely before optimizing rather than improvise. *Diagnostic — identify which matching variant is being asked*: the move is to sort the problem into the right target, because the variant names — *maximum* (most edges), *minimum-cost weighted* (the assignment problem), *maximal* (merely unextendable), *perfect* (every vertex covered) — are distinct objectives with distinct algorithms. The analyst reasons from "I need every vertex covered at least cost" to "this is a minimum-cost perfect matching — the Hungarian algorithm, not a bare maximum-cardinality routine," so the move is to name the variant to route the problem to its characterization and method. *Boundary-drawing (the signature move) — split on bipartite versus non-bipartite to select the toolkit*: the decisive move is to check the one parameter that selects the whole downstream apparatus — is the graph bipartite? The analyst reasons from "bipartite" to "augmenting-path methods, the Hungarian algorithm for weights, reduction to max-flow, König's matching–vertex-cover identity, and a clean half-integral matching polytope all apply," and from "non-bipartite" to "the problem demands Edmonds's blossom machinery for shrinking odd cycles and the odd-set inequalities that complicate the polytope," so the move is to read the governing theorems and the applicable polynomial-time algorithm off that single bit. *Diagnostic — convert the existence question into a structural verdict with a certificate*: the move is to refuse to search blindly for a perfect matching and instead ask "what obstructs one?", answering with a named, checkable condition that comes with a witness. The analyst reasons from "no perfect matching appears" to "in a bipartite graph, Hall's marriage condition fails — exhibit a subset of one side with too few neighbors; in a general graph, Tutte's odd-component condition fails," and from "this matching cannot be enlarged" to "König supplies a minimum vertex cover certifying the maximum size," so the move is to produce a deficiency certificate or covering witness rather than report a bare negative. The boundary on every move is the explicit-graph, vertex-disjoint structure the concept rests on: the variant routing, the bipartite split, and the obstruction certificates all presuppose the problem genuinely reduces to a largest pairwise-disjoint edge set on a stated graph, so the move where the real problem has constraints matching cannot express (preferences inducing stability rather than cardinality, three-way groupings, capacities beyond one-per-vertex) is to recognise that plain matching does not capture it and to reach for the appropriate generalization (stable matching, hypergraph matching, $b$-matching) rather than force the basic model.

knowledge_transfer:
Within mathematics and computer science the matching concept transfers *as mechanism*, and its apparatus — the variant taxonomy (maximum / minimum-cost weighted / maximal / perfect), the bipartite-versus-non-bipartite split that selects the toolkit, the existence certificates (Hall, Tutte, König), and the algorithms (augmenting paths, the Hungarian method, Edmonds's blossom shrinking, reduction to max-flow) — carries across the subfields without translation. The assignment problem (workers to jobs at least cost) is bipartite weighted matching; exam, machine, and timetable scheduling reduce to matching on constructed graphs; matching sits at the polynomial-solvable frontier whose extensions (3-D matching, constrained weighted bipartite) sharpen the NP-hard boundary; and it is the standard exposition for augmenting-path and LP-relaxation arguments. The diagnostic moves — fix the graph and objective, name the variant, split on bipartiteness, produce a deficiency or covering certificate — all port across these because each is genuinely a largest-pairwise-disjoint-edge-set problem on a stated graph. This is the within-domain transfer of a mathematical object: it travels as the same machinery wherever a problem truly reduces to vertex-disjoint edges.

Beyond mathematics the *object* does not travel; what travels is the cross-substrate *pattern* the term loosely evokes, and that is case (B) with an important twist — the pattern routes to *different parents depending on the case*, not to one. When preferences and mutual fit matter — labor and job markets, school choice (deferred acceptance), marriage markets (Gale–Shapley), kidney exchange (Roth's chains and cycles), residency matching (NRMP) — the operative prime is `two_sided_matching` (stable pairings between two sides under each side's preferences), which is in fact a *different mathematical object* from graph matching: stability, not cardinality or weight, is the target. When the problem is one-sided optimisation, it reads instead as `allocation` or `prioritization` (assigning a scarce resource) or as `optimization` / a scheduling pattern treated combinatorially. So the honest cross-domain lesson is to carry *those* primes — and indeed, when people informally say "matching" across domains, the prime they usually mean is `two_sided_matching`, not the graph-theoretic disjoint-edge-set. The matching object's own cargo — the Hall/König/Tutte characterizations, the blossom algorithm, the matching polytope and its odd-set inequalities — is mathematical machinery that does not generalise; invoking "matching" elsewhere either points at one of those existing primes or borrows the word without the structure, so the term carries no additional structural commitment beyond what `two_sided_matching` / `allocation` / `optimization` already supply (see *Structural Core vs. Domain Accent*).