Script Activation

one_liner: Trigger, from a single environmental or linguistic cue, the loading of a stored ordered event sequence with role and prop slots and default fillers, which then gap-fills comprehension, pre-allocates processing, and scaffolds action at once.

core_idea: Script activation is the cognitive process by which an environmental or linguistic cue triggers retrieval of a stored stereotyped event sequence — a script — that then supplies expectations about what steps occur next, in what order, by whom, and with what props. A script in Schank and Abelson's 1977 formulation is a structured knowledge representation of a conventionalised, temporally ordered event sequence (the restaurant script, the doctor-visit script, the airline-boarding script), with fixed role slots (host, customer), prop slots (menu, bill), preconditions, and default fillers for unspecified elements. Activation occurs at the moment a cue in the world or in discourse — a host approaching with menus, the phrase "John went to a restaurant and ordered" — matches the script's recognition conditions well enough to load the template into working memory. Once loaded, the script does three things simultaneously: it generates a prediction stream about likely next events that is used to fill comprehension gaps without explicit inference; it pre-allocates processing resources toward the expected inputs, making expected elements easier to process and unexpected ones harder; and it provides a ready action scaffold that reduces the decision load of navigating the familiar situation. Deviation from the activated script produces a characteristic violation response — surprise, disfluency, a search for the applicable alternative script, or a repair move that reconciles the deviation with the loaded template. When two candidate scripts are both partially cued, the resulting competition is resolved by whichever template's recognition conditions are better matched, with the losing script remaining partially active and available for rapid switch if the winner's predictions fail.

scope_of_application:
Script activation lives across the cognitive-science, discourse-comprehension, and interaction-design subfields that study stored event sequences; its reach is *within* that domain — its activation dynamics (dormant-versus-loaded, competition, violation-and-repair) presuppose a comprehending mind. The substrate-neutral "cue invokes an ordered, role-bound, defaulted template" pattern belongs to its `schema` / `sequencing` / `role` / `pattern_completion` parents, not to the named process.

- **Cognitive psychology** — the home turf: the classic script literature (restaurant, doctor-visit, theatre scripts) within the broader schema-theoretic tradition.
- **Reading comprehension** — script-based inference (reading "John ordered" supplies the sitting, the menu, the bill) underwrites discourse cohesion without explicit inference.
- **HCI and interaction design** — flow design implicitly leverages user scripts, and violating an entrenched one (a relocated elevator button) breaks usability.
- **Classical and modern natural-language understanding** — Schank–Abelson scripts and story-understanding programs (SAM, FRUMP), revived in modern LLM event-schema research.
- **Education** — classroom routines act as shared scripts whose reliable invocation reduces transaction costs.
- **Anthropology and sociology** — cultural scripts for greetings, weddings, and dispute resolution (Goffman's interaction order), a within-cognition-adjacent extension of the same schema-subtype.

structural_signature:
**Sig role-phrases:**

- **the stored sequence template** — a conventionalized, temporally ordered event sequence (restaurant, doctor-visit, boarding) with precedence relations among steps
- **the role and prop slots** — fixed positions for who acts (host, customer) and what objects appear (menu, bill), plus preconditions
- **the default fillers** — pre-stored values for unspecified slots, drawn from prior experience, that supply unstated content
- **the triggering cue** — an environmental or linguistic signal (a host with menus, "John went to a restaurant and ordered") matching the template's recognition conditions
- **the activation moment** — the cue loading the template from dormant background knowledge into working memory, where it shapes current processing
- **the prediction stream** — the loaded script emitting expectations about likely next events, gap-filling comprehension, pre-allocating processing toward expected inputs, and scaffolding action at once
- **the violation response** — surprise, disfluency, repair, or search for an alternative when input deviates from the loaded template's predictions, signaling *which* script was running
- **the script competition** — two partially-cued templates resolved by best recognition-condition match, the loser left primed for a fast switch if the winner's predictions fail

what_it_is_not:
- **Not mere possession of the knowledge.** "The person knew about restaurants" is not script activation; the explanatory action is the *cue-trigger moment* that loads dormant background knowledge into working memory, where it shapes current processing. Until a cue matches the template's recognition conditions, the script predicts, gap-fills, and scaffolds nothing.
- **Not a category schema or single-slot frame.** A script is specifically the *temporally ordered, role- and prop-structured* variety of schema — a sequence of steps with default fillers and precedence relations. A category schema (bird-schema) classifies but predicts no next step, and a single-slot frame lacks the sequence the script's inferences depend on.
- **Not the enacted routine or habit.** The script is the *representational template* — stored steps, slots, defaults; the routine is the realized performance that may run off it. One is the knowledge structure loaded on a cue, the other the behavior; the violation response and competition are properties of the loaded template, not of the enactment.
- **Not just the downstream expectation.** Expectation is one consequence; the loaded script does three jobs at once — gap-filling comprehension, pre-allocating processing toward expected inputs, and scaffolding action. Reducing it to "what the person expects next" misses that comprehension and action fall out of the same single loaded structure.
- **Not a workflow engine selecting a template.** A protocol with default fields or a workflow engine with ordered stages and role assignments instantiates the parents `schema`/`sequencing`/`role`/`pattern_completion`, not script activation — it has no dormant-versus-loaded distinction in working memory, no surprise-and-repair on deviation, no primed loser standing by. The cross-domain "a cue invokes an ordered template with role slots and defaults" lesson belongs to those parents; the named concept requires the activation dynamics.

clarity:
Naming script activation makes two things legible that schema theory's broader vocabulary leaves blurred. The first is a distinction within stored knowledge itself: a *script* is the *temporally ordered, role-structured* variety of schema — a sequence of events with slots for who acts, what props appear, and in what order — as opposed to a category schema (a bird-schema classifies; it predicts no next step) or a single-slot frame. Calling out that a piece of knowledge is a script tells the analyst that its content is a sequence with default fillers and precedence relations, which is precisely what licenses the inferences that make discourse cohere: reading "John went to a restaurant and ordered" lets a comprehender supply the sitting, the menu, and the bill without any of them being stated, because the script's defaults fill the gaps.

The second is the work the word "activation" does: it locates the explanatory action at the *cue-trigger moment* that separates dormant background knowledge from a template actually loaded into working memory and shaping current processing. That sharpening lets the field ask questions that "the person knew about restaurants" cannot frame — what cue matched the recognition conditions, what is now being predicted, and what happens when the input deviates. It makes the *violation response* (surprise, disfluency, the search for an alternative script) diagnostic evidence that a specific template was loaded, and it turns ambiguity into a tractable *competition*: when two scripts are partly cued, the question becomes which template's recognition conditions matched better and whether the loser stayed primed for a fast switch. Comprehension, expectation, and action-scaffolding thus become consequences of one loaded structure rather than separate faculties.

manages_complexity:
A comprehender or a researcher modeling comprehension faces an apparent combinatorial blow-up: ordinary discourse and ordinary situations are radically underspecified, yet understood without effort. "John went to a restaurant and ordered" never states that he sat, was handed a menu, ate, or paid, and in principle the gaps could be filled by an unbounded chain of inference; a person entering a familiar setting could in principle re-deduce from scratch who acts, what objects are involved, and what comes next. Script activation compresses that open-ended inference problem by positing that a single cue loads one stored template whose ordered steps, role slots, prop slots, and *default fillers* already encode the situation's regularities — so the comprehender carries the script's defaults rather than computing each missing element, and the analyst predicts what will be understood, what processed easily, and what done next by reading off the loaded template instead of tracing a bespoke inference for every gap. The high-dimensional "what does this person infer here" question collapses to "which script is active, and what are its slots and defaults."

The Clarity's relocation of the action to the cue-trigger moment is what gives the compression its small, trackable parameter set and its branch structure. Once the explanatory unit is *which template is loaded*, three things the field would otherwise treat separately — gap-filling comprehension, the ease/difficulty profile of processing expected versus unexpected inputs, and the action scaffold that lowers decision load — all fall out of the one loaded structure, so the researcher tracks a single state (the active script) rather than three faculties. The branches are equally definite: the recognition-condition match fixes *which* script loads; a deviation from its predictions fires the diagnostic *violation response* (surprise, disfluency, repair, search for an alternative), signalling exactly which template was running; and ambiguity resolves as a *competition* settled by which template's conditions matched better, with the loser left primed for a fast switch if the winner's predictions fail. So instead of modeling each underspecified situation as its own inference problem, the analyst carries one loaded template indexed by its recognition conditions, slots, and defaults, and reads off comprehension, expectation, action, and the response to surprise from that single structure and its small set of forks — the move from unbounded situational inference to a stored sequence-template with a few governing parameters.

abstract_reasoning:
Script activation supports a compact family of inferences in schema theory, discourse comprehension, and interaction design, each flowing from the loaded sequence-template and its slots.

*Diagnostic — reason from behaviour back to the active template.* The framework lets the analyst infer *which script is loaded* from observable signatures rather than asking it directly. From a comprehender who supplies unstated content — reads "John went to a restaurant and ordered" and reports that he sat, was handed a menu, ate, and paid — infer that a specific template was loaded and its *default fillers* did the gap-filling; the inferred content reveals the script's slots. The sharpest diagnostic is the *violation response*: surprise, disfluency, a double-take, a repair move, or a search for an alternative script signals that a particular template was running and that the input deviated from its predictions — so the violation is evidence *which* script was active, read off from which expectation was breached. The framework also makes ambiguity diagnosable as *competition*: when behaviour shows hesitation between two readings, infer that two scripts were partially cued and that recognition conditions had not yet settled which template wins, with the loser left primed; a rapid switch when the winner's predictions fail confirms the loser stayed available. From an *expert-versus-novice* contrast — a first-timer who must deliberate each step where a regular flows through — infer the regular has a loaded script supplying the scaffold the novice lacks.

*Interventionist — reason from the recognition conditions and slots to what changes behaviour, with a predicted direction.* Because activation hinges on a cue matching a template's recognition conditions, the levers act on cues, on the match, and on the template's availability, each carrying a directional prediction. Supply or strengthen the triggering cue (a host with menus, a kiosk that looks like a check-in desk): predicted to load the intended script earlier and smooth processing of the expected steps. Keep a new flow *close to an entrenched script* (in interface or service design): predicted to let the user's existing template carry the interaction with low decision load; conversely, a design that silently violates an entrenched script is predicted to produce the violation response — confusion, delay, lowered satisfaction — exactly where the deviation sits. Signal a required deviation explicitly, or pre-train the user into the new script before deprecating the old: predicted to pre-empt the mismatch by loading the correct template in advance, so the violation never fires. The interventionist move is a reading: identify whether the goal is to load a script, keep within one, or replace one, and predict that acting on the matching cue or the template's availability moves comprehension and action in the named direction; residual confusion after a cue is supplied diagnoses that the recognition conditions were not actually met.

*Boundary-drawing — reason about when the concept applies.* Script activation is in force only where the stored knowledge is a *temporally ordered, role- and prop-structured sequence with default fillers* and a cue matches its recognition conditions; a category schema (bird-schema) classifies but predicts no next step and so does not activate as a script, and a single-slot frame lacks the sequence the inferences depend on. It also requires the *cue-trigger moment*: dormant background knowledge that has not been loaded into working memory shapes nothing — "the person knew about restaurants" is not script activation until a cue loads the template. The boundary inference runs: if no ordered template with defaults is being predicted from, or if no cue has matched recognition conditions to load one, the gap-filling, the violation response, and the competition the concept explains will not appear, and a different schema construct is at work.

*Predictive / order-of-events.* The concept fixes a sequence — a cue (environmental or linguistic) matches a script's recognition conditions → the template loads into working memory → it emits a prediction stream about next events, pre-allocates processing toward expected inputs, and provides an action scaffold → expected elements are processed easily and unexpected ones with difficulty → a deviation from the predictions fires the violation response (surprise, disfluency, repair, search for an alternative) → an ambiguous cue resolves as competition settled by best match, loser left primed for a fast switch. This ordering licenses predictions: that expected steps are easier and unexpected ones harder *because* a template was loaded (a processing-asymmetry prediction, not a separate faculty); that gap-filling, expectation, and action-scaffolding co-occur because they fall out of one loaded structure; and that a missing script (a stranger's first visit to an unfamiliar setting) predicts effortful step-by-step deliberation where a held script predicts fluent navigation — so the presence or absence of the loaded template, not general intelligence, predicts the smoothness of comprehension and action.

knowledge_transfer:
Within cognitive science, discourse comprehension, and interaction design script activation transfers *as mechanism*, with the cue-loads-an-ordered-template-with-defaults process and its activation dynamics as the portable core. The Schank-Abelson script literature, script-based reading-comprehension inference (reading "John ordered" and supplying the sitting, menu, and bill), classical story-understanding AI (SAM, FRUMP, revived in modern LLM event-schema work), HCI flow design, classroom routines, and cultural scripts for greetings and ceremonies are all instances of one mechanism: a cue matches a template's recognition conditions, the ordered, role- and prop-structured template loads into working memory, and it then gap-fills comprehension, pre-allocates processing, and scaffolds action at once. So the diagnostics carry intact across these subfields (a comprehender supplying unstated content reveals the loaded template's slots; a violation response — surprise, disfluency, repair, search for an alternative — signals *which* script was running; ambiguity resolves as competition settled by best match, the loser left primed), and so do the interventions (supply or strengthen the triggering cue; keep a new flow close to an entrenched script; signal a required deviation or pre-train the new script before deprecating the old). The within-domain transfer is the activation mechanism itself moving from the restaurant script to the comprehension lab to the check-in kiosk.

Beyond the cognitive substrate the picture is the third category, and the carrier is a small family of parents the seed identifies precisely. Script is best understood as a *named subtype of `schema`* — the sequential, temporally-extended, role-bearing variety — and the substrate-independent residue (cue → ordered template → role/prop binding → default fillers → expectation → violation) is composed of parents that travel on their own: `schema` (structured knowledge frameworks), `sequencing` (ordered steps under precedence), `role` (a bundle of expected behaviors attached to a position), and `pattern_completion` (inferring missing structure, of which script-driven gap-filling is a textbook case). Those parents recur across substrates — a workflow engine with ordered stages and role assignments, a protocol with default fields, a template system that fills unspecified slots — and where the cross-domain lesson is "a cue invokes an ordered template with role slots and defaults," it belongs to that `schema`+`sequencing`+`role`+`pattern_completion` composition. What stays home-bound is what is distinctively *script-theoretic*: the *activation dynamics* — the cue-trigger moment that separates dormant background knowledge from a loaded template, the competition between partially-cued scripts, the violation response with its surprise and repair — together with the cognitive-psychology comprehension paradigm in which they were established. A workflow engine selecting a template is not undergoing "script activation": it has no dormant-versus-loaded distinction in working memory, no surprise-and-repair on deviation, no primed loser standing by — it is instantiating the `schema`/`sequencing` parents. Calling it script activation would import the comprehension-and-violation dynamics that have no referent there — analogy at the level of "a cue loads an ordered template," mechanism only at the level of the parents. The related senses in cultural psychology and sociolinguistics (cultural and conversational scripts) and clinical work (relational schemas) cohere under the same schema-subtype reading and are within-cognition-adjacent extensions, not cross-substrate transfers. So the honest move is layered: within cognition the activation mechanism and its full diagnostic/intervention map travel across comprehension, AI, HCI, and cultural-routine settings; the abstract "cue invokes an ordered, role-bound, defaulted template" lesson belongs to `schema`/`sequencing`/`role`/`pattern_completion` wherever a non-cognitive system instantiates a stored sequence; but "script activation," as named — with its cue-trigger loading, competition, and violation response — is reserved for the cognitive case (see *Structural Core vs. Domain Accent*).


Seamount Effect

one_liner: Read submarine relief as a first-order control on ocean productivity via a fixed causal chain — relief plus flow yields a perturbation that mixes and upwells nutrients, raising production and aggregating life — that fires only when current, stratification, height, and latitude align.

status: draft

core_idea:
The seamount effect is the suite of physical and biological consequences that arise when an isolated submarine mountain rising steeply from the abyssal floor intercepts ambient ocean currents, generating localized flow perturbations that produce disproportionate productivity and biological aggregation over and around the feature. The physical mechanism runs through several coupled pathways: (i) current impingement on the seamount summit creates a Taylor column — a quasi-stationary rotating fluid column capping the topography — which traps surface-produced particles in a recirculating cell above the seamount; (ii) tidal and geostrophic flow over the steep flanks generates internal waves and internal tides, which break in the water column and drive enhanced turbulent mixing that upwells nutrient-rich deep water into the euphotic zone; (iii) topographic steering concentrates horizontally advected plankton and zooplankton, which accumulate in the elevated flow convergence zones around the seamount summit. The combined effect converts a far-field current that passes flat seafloor without notable consequence into a localized productivity engine.

The biological consequence is aggregation at multiple trophic levels: primary production elevated by nutrient upwelling supports zooplankton blooms, which in turn support concentrations of mesopelagic fish, squid, pelagic tuna and billfish, sharks, marine mammals, and seabirds that track the seamount's food-web productivity. Benthic communities on seamount summits include deep-water corals and diverse invertebrates sustained by the enhanced particle rain from above. This aggregation creates a fishing vulnerability signature: the same topographic forcing that concentrates fish makes them catchable with high efficiency, explaining why seamount-targeted fisheries — orange roughy and alfonsino on South Pacific seamounts, pelagic tuna over the Emperor Seamount Chain — can collapse within years of discovery.

scope_of_application:
The seamount effect lives across the physical-biological and fisheries subfields of marine science; its reach is within that domain, the genuinely shared physics in other flow-over-topography systems (mountain waves, fluvial scour) riding on the parent `topographic_forcing` (static geometry perturbing a flowing medium) rather than on the seamount effect itself.

- **Physical-biological oceanography** — the home turf: the named control linking bathymetry to productivity through the causal chain (relief plus flow → Taylor column / internal tides → mixing and upwelling → nutrients → production → aggregation).
- **Fisheries science and management** — the vulnerability signature: the coupling of aggregation to catchability explaining the rapid collapse of newly charted seamount fisheries (orange roughy, alfonsino, tuna over the Emperor Seamount Chain).
- **Marine spatial planning and conservation** — feature-targeted protection: siting marine protected areas around the specific topographic features that activate the chain, rather than spreading protection uniformly.
- **Internal-wave and mixing oceanography** — internal-tide generation at the steep flanks, a pathway feeding the deep-ocean mixing budget.
- **Benthic and deep-sea ecology** — summit communities: deep-water corals and invertebrates sustained by the enhanced particle rain from the elevated production above.
- **Coastal upwelling dynamics** — the same static-relief-perturbs-flow chain at smaller scale: promontories and capes driving lee-side upwelling, to which the activation reasoning ports directly.

structural_signature:
**Sig role-phrases:**

- **the ambient flow** — far-field ocean currents and tides that would pass flat seafloor without notable consequence
- **the static relief** — an isolated submarine mountain rising steeply from the abyssal floor, projecting into the flow
- **the activation parameters** — ambient current strength, stratification, the seamount's height relative to the mixed layer, and latitude through the Coriolis effect, jointly deciding whether the chain fires
- **the flow perturbation** — current impingement generating a quasi-stationary Taylor column over the summit, plus internal waves and internal tides off the steep flanks
- **the resource-supply pathway** — internal-tide breaking driving turbulent mixing that upwells nutrient-rich deep water into the euphotic zone, with topographic steering concentrating advected plankton
- **the production-and-aggregation cascade** — elevated primary production feeding zooplankton blooms, mesopelagic fish, tuna, sharks, mammals, and seabirds, plus benthic corals sustained by enhanced particle rain
- **the vulnerability signature** — the same forcing that aggregates fish making them catchable at high efficiency, so concentration and exploitability are one coupled output (rapid fishery collapse on discovery)
- **the activation contrast** — a physically similar feature in weak flow or a deep mixed layer leaving the chain dormant and the water barren, explaining why not every seamount is a hotspot

what_it_is_not:
- **Not a property of every seamount.** A submarine mountain does not, by existing, produce a hotspot; the chain fires only when the activation parameters align — sufficient ambient current, the right stratification, height relative to the mixed layer, a favorable latitude through the Coriolis effect. A physically similar feature in weak flow or under a deep mixed layer leaves the chain dormant and the water barren, which is exactly why not all seamounts are hotspots.
- **Not a single mechanism.** It is a *suite* of coupled physical pathways feeding one causal chain — Taylor-column trapping, internal-tide breaking that mixes nutrients upward, and topographic steering that concentrates plankton — not one effect. Naming any one pathway (say, "upwelling") captures only a link, not the chain from relief-plus-flow through nutrients to multi-trophic aggregation.
- **Not a closed circulation that drifts.** The Taylor column is an *attached*, quasi-stationary feature anchored over the topography, not a closed eddy or gyre that travels with the flow. The productivity is pinned to a fixed location on the seafloor, which is what makes fish distribution map onto the bathymetric chart.
- **Not productivity from the rock.** The seamount supplies no nutrients of its own; the enrichment comes from the *flow perturbation* its relief induces — mixing and upwelling that bring deep nutrient-rich water into the euphotic zone. The mountain is an obstacle that reorganizes the flow, not a source of fertility.
- **Not aggregation and catchability as separate facts.** The same topographic forcing that concentrates fish makes them catchable at high efficiency — one coupled output, not two independent properties. That coupling is why newly charted seamount fisheries (orange roughy, alfonsino) collapse within years of discovery, rather than concentration being benign and exploitability a separate matter.

clarity:
Naming the seamount effect resolves submarine topography from passive backdrop into a first-order control on ocean productivity, and it does so by making one causal chain explicit: static relief plus ambient flow yields a flow perturbation, which drives mixing or upwelling, which supplies nutrients, which raises production, which aggregates life. Each link is empirically tractable, so the chain explains why fish distribution maps so well onto bathymetric charts — "where to find tuna" tracks the topography because the topography is what concentrates the food web. The practitioner can now ask not just "is there a seamount here?" but "does this seamount activate the chain?", and the concept tells them what governs the answer: ambient current strength, stratification, the seamount's height relative to the mixed layer, and latitude through the Coriolis effect. That is why some seamounts are hotspots and physically similar ones are not.

The concept's sharpest practical contribution is the *vulnerability signature*: the same topographic forcing that concentrates fish also makes them catchable at high efficiency, so concentration and exploitability are produced by a single mechanism rather than being independent facts. That coupling is what explains the otherwise puzzling speed with which newly discovered seamount fisheries — orange roughy, alfonsino — collapse within years of being charted, and it converts a bathymetric map into a predictor of where aggregation, and therefore fishing risk, will appear. For management it reframes marine-protected-area siting from a question about area to a question about which topographic features carry the effect.

manages_complexity:
Why one patch of open ocean teems with tuna, sharks, seabirds, and deep-water corals while physically similar water nearby is barren looks, untamed, like a problem requiring the full coupled physics and ecology of each site — currents, internal waves, mixing, nutrient supply, primary production, and the foraging behavior of half a dozen trophic levels. The seamount effect compresses that sprawl into one causal chain whose links run in fixed order: static relief plus ambient flow yields a flow perturbation, which drives mixing or upwelling, which supplies nutrients, which raises production, which aggregates life. Because the chain is sequential and each link is empirically tractable, an analyst need not model the whole physical-biological system; tracking whether the chain *fires* suffices, and it fires as a function of a small parameter set — ambient current strength, stratification, the seamount's height relative to the mixed layer, and latitude through the Coriolis effect. That is what lets fish distribution be read off a bathymetric chart: "where to find tuna" maps onto topography because topography, via the chain, is what concentrates the food web, collapsing a productivity-mapping problem into a question of which charted features activate the perturbation.

The branch structure is sharp at both ends. Run the parameters: a tall seamount intercepting a strong current in stratified water at a favorable latitude activates the Taylor column, internal-tide mixing, and topographic steering, and the analyst reads off a hotspot; a physically similar feature in weak flow or a deep mixed layer leaves the chain dormant and the analyst reads off barren water — resolving the otherwise puzzling fact that not every seamount is a hotspot. The deeper compression is that concentration and exploitability are *not* independent facts to be assessed separately but a single coupled output: the same topographic forcing that aggregates fish makes them catchable at high efficiency, so the *vulnerability signature* reads straight off the same chain that predicts the aggregation. This is what makes the rapid collapse of newly charted seamount fisheries — orange roughy, alfonsino, within years of discovery — a predictable consequence rather than a surprise, and it converts marine-protected-area siting from a question about area into a question about which topographic features carry the effect. The whole tangle reduces to a chain, a four-parameter activation test, and the coupling of aggregation to catchability.

abstract_reasoning: The seamount effect licenses a set of moves on submarine topography and the productivity it organizes, all routed through the fixed-order causal chain and the four-parameter test of whether it fires. *Predictive (the signature move) — read productivity off bathymetry via the chain*: the foundational move is to treat submarine relief not as passive backdrop but as a first-order control, and to predict biological aggregation by running one sequential chain — static relief plus ambient flow yields a flow perturbation, which drives mixing or upwelling, which supplies nutrients, which raises production, which aggregates life. Because the chain is sequential and each link is empirically tractable, the analyst reasons from a bathymetric chart directly to where the food web concentrates: "where to find tuna" maps onto topography because topography, via the chain, is what concentrates the food web. So the move is to read a productivity map off a depth map rather than survey the open ocean blind. *Boundary-drawing — test activation with the four parameters, explaining why not every seamount is a hotspot*: the decisive move is to ask not "is there a seamount here?" but "does this seamount *activate* the chain?", and to answer from a small parameter set — ambient current strength, stratification, the seamount's height relative to the mixed layer, and latitude through the Coriolis effect. The analyst reasons from "a tall seamount intercepting a strong current in stratified water at a favorable latitude" to "the Taylor column forms, internal tides break and mix nutrients up, topographic steering concentrates plankton — a hotspot," and from "a physically similar feature in weak flow or a deep mixed layer" to "the chain stays dormant — barren water." So two physically similar features get opposite predictions from the activation test, resolving the puzzle that not all seamounts are hotspots. *Predictive — couple aggregation to catchability (the vulnerability signature)*: the most consequential move is to recognize that concentration and exploitability are *not* independent facts to assess separately but a single coupled output of the same topographic forcing — the very mechanism that aggregates fish makes them catchable at high efficiency. So the analyst reasons from "this seamount activates the effect" to "fish will both concentrate here *and* be highly catchable here," and predicts the rapid collapse of a newly charted seamount fishery — orange roughy, alfonsino, within years of discovery — as a determinate consequence rather than a surprise. The reasoning runs from one mechanism to both the aggregation and the fishing risk at once. *Interventionist — site protection by the feature that carries the effect*: the move reframes marine-protected-area design from a question about *area* to a question about *which topographic features activate the chain*, so the analyst reasons from "this feature carries the effect and concentrates both the food web and the fishery" to "protect this feature specifically," targeting the few activating seamounts rather than spreading protection uniformly. The boundary on every move is the substrate-specific apparatus the chain rests on — Taylor columns, internal-tide generation, buoyancy, and Coriolis dynamics — so the move where those mechanics are absent is to recognize that a fixed obstacle in a flow may concentrate something, but not via the seamount effect, and the four-parameter activation test no longer applies.

knowledge_transfer:
Within marine science the seamount effect transfers *as mechanism*, and its apparatus — the fixed-order causal chain (relief plus flow → perturbation → mixing/upwelling → nutrients → production → aggregation), the Taylor-column and internal-tide physics, the four-parameter activation test, and the aggregation-couples-to-catchability vulnerability signature — carries across the relevant subfields. In **physical-biological oceanography** it is the named control linking bathymetry to productivity. In **fisheries science and management** the vulnerability signature explains the rapid collapse of newly charted seamount fisheries (orange roughy, alfonsino) and reframes marine-protected-area siting around which features carry the effect. Its closest marine kin — **coastal upwelling at promontories and capes** (flow separation driving lee-side upwelling) and **estuarine bathymetric highs** (localized mixing fronts) — are the same static-relief-perturbs-flow chain at smaller scale, and the activation reasoning ports to them directly. The vocabulary, the chain, and the diagnostics carry — mechanism travelling within its home domain.

Beyond marine science the transfer splits at the boundary of the fluid-dynamical substrate. To other **flow-over-topography systems** it transfers *as mechanism, not metaphor*, because the governing physics is genuinely shared: topographically forced atmospheric weather (mountain waves, lee cyclones, orographic precipitation enhancement, foehn winds) and fluvial obstacle dynamics (boulders and log jams generating turbulence and scour pools that concentrate fish) run the same *static feature in a flow field produces a local perturbation that concentrates flux around the feature*. Read at the right grain this is case (B): what recurs is that general pattern — a candidate parent the seed flags as `topographic_forcing` (static substrate geometry perturbing a flowing medium to concentrate flux), itself resting on `flow` + obstacle + spatial `concentration` — and the cross-domain lesson should carry *that* parent, not "seamount effect," because the marine cargo (Taylor-column theory, internal-tide generation, the Burger number, buoyancy and Coriolis dynamics, the fishery-collapse story) stays home in the ocean. Note that even within case (B) the *specific* perturbation regime does not port: a mountain wave is not a Taylor column; only the higher-level "obstacle concentrates flux" survives. Push past flow-over-topography entirely — to "seamounts" in supply chains, organizational information flow, or platform governance — and the transfer collapses to case (A), pure metaphor: it trades on the *image* of a prominent fixed feature concentrating moving traffic while the mechanics (buoyancy, Coriolis, internal waves) do not transfer at all. The honest move is to mark such uses as analogy and route the genuine flow-over-topography recurrence to the topographic-forcing parent (see *Structural Core vs. Domain Accent*).


Search Algorithm

one_liner: Solve a problem by casting it as a state space explored with a frontier-ordering strategy, so completeness, optimality, and cost are read off the strategy's name — and an admissible heuristic buys a narrower search without sacrificing the optimal answer.

origin_subdomain: algorithms

core_idea:
A search algorithm is the computer-science discipline of systematically exploring a *state space* — a graph or tree of candidate states too large to enumerate exhaustively — to find a state, path, or configuration satisfying a specified goal or objective, using an *exploration strategy* that decides which state to examine next. The structural commitments shared by all members of the family are: (a) a *state space* defined by an initial state, a *successor function* that generates neighbouring states from any given state, and a *goal test* or objective function distinguishing solution states from non-solution states; (b) a *frontier* (open list) of states generated but not yet expanded; (c) a *strategy* that orders the frontier to determine the next state to expand.

The classical taxonomy divides strategies by whether they use problem-specific information about proximity to the goal. *Uninformed* (blind) strategies — breadth-first search (BFS), depth-first search (DFS), uniform-cost search, iterative deepening depth-first search (IDDFS) — use only the structure of the state space; they differ in the data structure governing the frontier (queue, stack, priority queue keyed by path cost) and in their completeness and optimality guarantees. *Informed* (heuristic) strategies — greedy best-first search, A*, weighted A*, iterative-deepening A* (IDA*) — augment the cost function with an *admissible heuristic* h(n), an estimated lower bound on the cost to reach a goal from state n; A* expands states in order of f(n) = g(n) + h(n) where g(n) is the cost of the path to n, and is both complete and optimal when h is admissible and consistent. Beyond tree and graph search, the family extends to *local search* strategies — hill climbing, simulated annealing, genetic algorithms, tabu search, beam search — that maintain one or a small set of current states and move through the space without tracking a frontier, trading completeness for scalability to spaces too large for systematic exploration.

The field was systematised by Allen Newell and Herbert Simon through the General Problem Solver in the late 1950s and formalised as the dominant framework for AI planning and automated reasoning. A* was introduced by Hart, Nilsson, and Raphael in 1968. Russell and Norvig's *Artificial Intelligence: A Modern Approach* (1995 and later editions) codified the taxonomy that now organises the undergraduate and graduate curriculum. The same state-space-and-strategy skeleton underlies motion planning in robotics (RRT, PRM, grid-based A*), constraint satisfaction (DPLL and its descendants in SAT solving), combinatorial optimisation (branch-and-bound, beam search), and game-tree search (minimax with alpha-beta pruning, Monte Carlo tree search).

scope_of_application:
The search-algorithm discipline lives across the subfields of computer science and AI that explore a too-large, machine-represented state space with a frontier-ordering strategy; its reach is within that domain. The broad activity of "finding something useful under uncertainty about where it is" does recur in hiring, diagnosis, and foraging, but that breadth belongs to the parent `search_and_retrieval` (with `exploration_vs_exploitation`, `optimization`, `heuristic`) — a hiring manager gains nothing from choosing BFS over A*, and the algorithmic taxonomy does not travel.

- **AI planning and automated reasoning** — the foundational home where Newell and Simon's General Problem Solver and the Russell-Norvig taxonomy organize uninformed and informed strategies (BFS, DFS, uniform-cost, A*, IDA*).
- **Robotics and motion planning** — configuration-space search for collision-free paths via RRT, PRM, and grid-based A*.
- **Constraint satisfaction and SAT/SMT solving** — DPLL with conflict-driven clause learning, backtracking, and restarts over assignment space.
- **Combinatorial optimization** — branch-and-bound and beam search exploring solution spaces with cost bounds.
- **Game-tree search** — minimax with alpha-beta pruning and Monte Carlo tree search over game states.
- **Metaheuristic optimization** — local-search strategies (hill climbing, simulated annealing, genetic algorithms, tabu search) for spaces too large to hold a frontier.

structural_signature:
**Sig role-phrases:**

- **the state space** — the graph or tree of candidate states, too large to enumerate exhaustively
- **the initial state** — where exploration begins
- **the successor function** — the generative moves producing neighboring states from any given state
- **the goal test / objective** — the predicate or function distinguishing solution states from non-solution states
- **the frontier (open list)** — the set of states generated but not yet expanded
- **the exploration strategy** — the rule ordering the frontier to decide what to expand next (the one variable that distinguishes otherwise-identical problems)
- **the informed/uninformed split** — the branch axis: whether the strategy uses problem-specific proximity information about distance to the goal
- **the admissible heuristic** — for informed strategies, the estimated lower bound h(n) on cost-to-goal; admissibility (never overestimates) plus consistency is the checkable property that preserves optimality
- **the guarantee profile** — the fixed axes each strategy is pinned to: completeness, optimality, time complexity, space complexity (read off the name, not by running it)
- **the local-search escape** — the fallback for spaces too large to hold a frontier: maintain one or few current states (hill climbing, simulated annealing, genetic algorithms, beam search), trading completeness for scalability
- **the termination interpretation** — what the manner of stopping implies: goal dequeued (solution, optimal under admissible h), frontier exhausted (provably no solution), budget/convergence reached (best-found-so-far, no guarantee)

what_it_is_not:
- **Not looking up a value in a list.** A search algorithm explores a *state space* — a graph or tree generated on demand by a successor function and too large to enumerate — not a stored collection scanned linearly or by binary search. The object is to find a state, path, or configuration satisfying a goal by deciding what to expand next, not to retrieve a known item from a container.
- **Not the broad search-and-retrieval pattern.** Casting hiring, diagnosis, or foraging as "search" invokes the substrate-independent `search_and_retrieval` prime (plus exploration-vs-exploitation); the search *algorithm* is the narrower exploration-strategy sub-pattern. The cross-domain reach belongs to the parent — a hiring manager gains nothing from choosing BFS over A* — so the algorithmic taxonomy does not travel the way the general activity does.
- **Not a choice that changes the problem.** The strategy orders the frontier; it does not alter the state space. The same maze, puzzle, or game tree is the identical computation under BFS, DFS, or A*, differing only in *what gets expanded next*. Treating the algorithm as part of the problem encoding confuses the exploration policy with the problem it explores.
- **Not "any heuristic helps."** An informed strategy preserves optimality only when its heuristic is *admissible* (never overestimates the true cost to a goal) and consistent; an inadmissible heuristic can make A* return a sub-optimal path. "Is my heuristic any good?" is a checkable property with a known payoff, not a matter of intuition — and a sharper *admissible* heuristic shrinks the search without surrendering optimality.
- **Not guaranteed to find the optimal solution, or any solution.** Completeness and optimality are strategy-dependent profiles, not universal properties: DFS is neither complete nor optimal in general, and local-search methods (hill climbing, simulated annealing) surrender completeness for scalability. A frontier exhausted means *provably no solution* in the searched space; a budget consumed means only best-found-so-far — the manner of termination fixes the result's epistemic status.
- **Not the same as optimization.** Optimization is the broader find-the-best problem and includes analytical solutions, gradient methods, and linear programming that are not state-space search; many search algorithms solve optimization problems, but search is specifically exploration of a state space by a frontier-ordering strategy. The two overlap without coinciding.

clarity:
The first clarifying act is the *state-space-and-strategy* decomposition itself. Once a problem is cast as an initial state, a successor function, a goal test, and a frontier-ordering strategy, an enormous variety of superficially unrelated tasks — pathfinding, puzzle-solving, theorem-proving, game play — are revealed to share one skeleton, and the only thing that distinguishes them is the strategy that orders the frontier. That separation lets a practitioner reason about the *exploration policy* independently of the *problem encoding*: the same maze becomes a different computation under BFS, DFS, or A* without the maze itself changing, so the question "how do I solve this?" sharpens into the answerable "what should I expand next, and why?"

The taxonomy's deeper clarity is that it makes the choice of strategy a decision about *guarantees* rather than a matter of taste. Naming a strategy pins down its profile along a fixed set of axes — completeness (will it find a solution if one exists?), optimality (will the solution be least-cost?), and time and space complexity — so a designer compares candidates by what each promises rather than by intuition. The informed/uninformed split sharpens this further: it isolates exactly what problem-specific knowledge buys, namely a heuristic estimate of distance-to-goal, and the conditions under which that knowledge is *safe* to trust. The crisp result that A* is optimal precisely when its heuristic is *admissible* (never overestimates) and *consistent* turns "is my heuristic any good?" into a checkable property with a known payoff, and explains why a better-informed heuristic narrows the search without sacrificing the optimality guarantee. The framework thus converts a vague "explore until you find it" into a structured set of questions — is the strategy complete, is it optimal, what does it cost, and is its heuristic admissible — whose answers are settled by the strategy's structure, not discovered by running it.

manages_complexity:
The discipline tames two distinct sprawls at once. The first is the sprawl of *problems*: pathfinding through a maze, solving a sliding puzzle, proving a theorem, planning a robot's motion, satisfying a Boolean formula, playing a board game, routing a delivery — superficially unrelated tasks, each with its own literature and its own apparent bespoke method. The state-space-and-strategy decomposition collapses that menagerie to a single skeleton: an initial state, a successor function, a goal test, a frontier, and a strategy that orders the frontier. Every one of those tasks is the same computation differing only in how its four problem-parts are filled in, so the analyst who has internalized the skeleton confronts not a hundred problem-types but one, parameterized. The maze, the proof, the game tree are one object wearing different encodings.

The second sprawl is the space of *methods*, and here the compression is sharper still because it replaces "try it and see how it behaves" with reading guarantees off a name. The countless ways one might explore a state space collapse to a small taxonomy whose members are each pinned to a fixed set of axes — completeness, optimality, time complexity, space complexity — so the designer does not run a strategy to learn its profile; the profile is a structural property the name carries. What the analyst tracks shrinks to a few coordinates: is the strategy informed or uninformed, what frontier data structure orders it (queue, stack, priority queue), and — for informed strategies — is the heuristic admissible and consistent. From those coordinates the qualitative outcome reads straight off: an uninformed BFS is complete and optimal for unit costs but exponential in space; A* with an admissible, consistent heuristic is complete and optimal and narrows the explored set as the heuristic sharpens; a local-search method like simulated annealing surrenders completeness for scalability to spaces too large to hold a frontier at all. The decision "how do I solve this?" reduces to a finite menu choice over guarantee-profiles, not an open-ended invention.

The branch structure that organizes the menu is the informed/uninformed split, and it is what isolates exactly what problem-knowledge buys and when it is safe. The split factors the choice into a small decision tree: if no proximity information is available, the designer is in the uninformed branch and trades completeness against space among BFS, DFS, uniform-cost, and iterative deepening on known terms; if a distance-to-goal estimate exists, the informed branch is open, and a single checkable property — admissibility (the heuristic never overestimates), with consistency — decides whether the optimality guarantee survives. "Is my heuristic any good?" stops being a matter of intuition and becomes a property with a known payoff: an admissible heuristic preserves optimality while a sharper one shrinks the search, so the two desiderata stop trading against each other. A practitioner therefore reasons about the whole field of state-space problems through one skeleton, a short list of strategy coordinates, and a single admissibility test — reading completeness, optimality, and cost off the structure of the chosen strategy rather than discovering them by executing it.

abstract_reasoning:
A search algorithm licenses a set of reasoning moves in algorithms and AI, all flowing from the state-space-and-strategy decomposition and from the taxonomy that pins each strategy to a fixed set of guarantees.

The signature *formulation* move casts an unfamiliar problem into the four canonical parts — an initial state, a successor function, a goal test (or objective), and a frontier ordered by a strategy. Once a maze, a sliding puzzle, a theorem-proving task, a robot's motion, a Boolean formula, or a board game is encoded this way, the reasoner recognizes it as the same computation differing only in how the four parts are filled in, and can reason about the *exploration policy* independently of the *problem encoding* — the same maze becomes a different computation under BFS, DFS, or A* without the maze changing. The move turns "how do I solve this?" into the answerable "what should I expand next, and why?"

The *strategy-selection-by-guarantee* move chooses an exploration policy by reading its profile off its name rather than by running it. Because each strategy is pinned to fixed axes — completeness (will it find a solution if one exists?), optimality (least-cost?), time complexity, space complexity — the reasoner compares candidates by what each *promises*: BFS is complete and optimal for unit costs but exponential in space; uniform-cost is optimal for general non-negative costs; DFS is space-cheap but neither complete nor optimal in general; A* with an admissible, consistent heuristic is complete and optimal. The decision reduces to a finite menu choice over guarantee-profiles, with the profile a structural property carried by the name, so the reasoner predicts a strategy's completeness, optimality, and cost before executing it.

A *branching / boundary-drawing* move organizes that menu by the informed/uninformed split and fixes when each branch is available. If no proximity information exists, the reasoner is in the uninformed branch and trades completeness against space among BFS, DFS, uniform-cost, and iterative deepening on known terms; if a distance-to-goal estimate exists, the informed branch opens; and if the space is too large to hold a frontier at all, the reasoner crosses into local search (hill climbing, simulated annealing, genetic algorithms, beam search), explicitly surrendering completeness for scalability. The split isolates exactly what problem-knowledge buys and routes the choice accordingly.

The *admissibility-diagnostic* move is the field's sharpest checkable inference: A* is optimal precisely when its heuristic is *admissible* (never overestimates the true cost to a goal) and consistent. So "is my heuristic any good?" becomes a property to verify rather than an intuition — the reasoner checks admissibility and concludes optimality is preserved, or finds the heuristic inadmissible and predicts that A* may return a sub-optimal path. From the same property comes an *interventionist* move on the heuristic: a sharper (more informed) admissible heuristic narrows the explored set without sacrificing optimality, so the reasoner improves the heuristic's tightness to shrink the search and predicts the reduction, with the two desiderata — optimality and speed — no longer trading against each other. A complementary move trades optimality deliberately for speed (weighted A*, greedy best-first), predicting a faster but possibly sub-optimal result, when the application can accept it.

Finally, a *termination-interpretation* move reads what each way of stopping implies: goal dequeued means a solution found (optimal under an admissible heuristic with A*); frontier exhausted means provably no solution exists in the searched space; budget consumed or convergence reached (in local search) means a best-found-so-far with no completeness guarantee. The reasoner infers the epistemic status of the result from the manner of termination, not merely from whether an answer was produced.

knowledge_transfer:
Within computer science and AI the search-algorithm discipline transfers *as mechanism*. The state-space-and-strategy formulation (cast a problem as initial state, successor function, goal test, and frontier-ordering strategy), the strategy-selection-by-guarantee move (read completeness, optimality, and time/space complexity off the strategy's name), the informed/uninformed branch, the admissibility diagnostic (A* is optimal exactly when its heuristic never overestimates and is consistent), and the termination-interpretation move all carry intact across the subfields that share the substrate. They apply unchanged to motion planning (RRT, PRM, grid A*), constraint satisfaction and SAT/SMT solving (DPLL with conflict-driven clause learning, backtracking, restarts), combinatorial optimization (branch-and-bound, beam search), game-tree search (minimax with alpha-beta, Monte Carlo tree search), and metaheuristic optimization (simulated annealing, genetic algorithms) — so a priority queue keyed by f(n)=g(n)+h(n), an admissible heuristic, or a completeness guarantee means the same thing whether the space is a maze grid, an assignment space, or a game tree. The transfer is mechanical here because each is the same substrate — a too-large state space explored by a frontier-ordering strategy — and the engineering content (data structures, heuristic admissibility, complete-versus-incomplete strategies) refers to the same machinery throughout.

Beyond computer science the transfer is case (B), a **shared abstract mechanism** that recurs while the algorithmic taxonomy stays home-bound. The general activity — *find something useful under uncertainty about where it is, by exploring a space* — is genuinely substrate-portable and recurs across domains as real instances: hiring and candidate sourcing, scientific hypothesis generation and experiment selection, medical differential diagnosis, equipment fault isolation, venture-deal pipelines, optimal foraging in animal behavior, web crawling. But what travels in these is *not* the search-algorithm-specific content; it is the broader primes the discipline instantiates — `search_and_retrieval` (the substrate-independent locate-something-in-a-space pattern, which already explicitly covers the algorithmic, cognitive-memory, biological-foraging, and information-retrieval cases), `exploration_vs_exploitation` (when to keep looking versus commit), and, where the goal is the best rather than any solution, `optimization` and `heuristic`. A hiring manager, a clinician, and a forager are each doing "search" in this broad sense, and the portable insight they share lives in those parent primes — the hiring manager gains no traction from distinguishing breadth-first from depth-first exploration of the candidate pool, and does not need to choose between BFS and A*. What stays strictly home-bound is everything that makes it a *search algorithm*: the frontier/open-list data structures, admissible-and-consistent heuristics, the completeness/optimality/complexity guarantee profiles, and the specific named strategies are computer-science engineering with meaning only where there is an explicit machine-represented state space and an exploration policy to run over it. So the candidate's breadth claim overlaps almost entirely with `search_and_retrieval`'s, and its distinctive content is the narrower exploration-strategy sub-pattern, which is exactly why it is a domain-specific abstraction (best filed as a CS-algorithmic sub-pattern under `search_and_retrieval`) and not a separate prime — adding it would duplicate the parent without adding cross-substrate force. The honest cross-domain lesson is to carry those parents (search-and-retrieval, exploration-vs-exploitation, optimization, heuristic); "search algorithm," as named, carries computer-science engineering baggage that does not and should not travel. (See *Structural Core vs. Domain Accent*.)


Hidden Label

one_liner: A string attached to a controlled-vocabulary concept that is indexed for retrieval but withheld from display — so a user typing a typo, deprecated synonym, or embarrassing prior name finds the concept while the result still shows only the sanctioned preferred term.

core_idea:
A hidden label is a string attached to a concept in a controlled vocabulary that is indexed for retrieval but deliberately withheld from public display. A user who types the typo, the deprecated synonym, the dialect variant, or the embarrassing prior name will find the concept, but the label that surfaces on the result is the sanctioned preferred term. The device separates two functions of a name that ordinary vocabularies conflate: *recognition during search* and *authority during presentation*.

In SKOS the mechanism is `skos:hiddenLabel`, the third tier below `prefLabel` (the displayed canonical name) and `altLabel` (additional displayed synonyms). Hidden labels carry the forms a system wants to accept as input — misspellings, retired terms, offensive historical names, purely phonetic guesses — without endorsing them as output. The asymmetry is structural: the retrieval index is wider than the display layer, absorbing the long tail of how users actually type while keeping the presentation surface clean and authoritative. Unlike an `altLabel`, a hidden label carries no display obligation and can be silently added or removed without altering what the system says back to the user. PubMed's MeSH thesaurus indexes "heart attack" as a hidden entry term for the preferred descriptor "Myocardial Infarction": the lay term is findable but not sanctioned, and the cited record shows only the preferred heading.

scope_of_application:
The hidden label lives across the retrieval and controlled-vocabulary subfields of information science; it applies wherever one substrate recurs — a retrieval index run over a catalog, with a separable index layer and display layer — and the genuinely cross-substrate version of the insight (recognition-canonical vs. display-canonical coming apart) belongs to its parents `markedness` and `aliasing`, not to this SKOS convention.

- **Thesauri and subject vocabularies** — the canonical use: MeSH entry terms, retired ERIC descriptors, and SKOS `hiddenLabel` strings indexed for retrieval but suppressed from the displayed heading, so "heart attack" finds "Myocardial Infarction" without endorsing the lay term.
- **Product search** — misspellings and competitor names mapped onto catalog items so a mistyped query lands on the product, while neither string appears on the product page.
- **Wiki redirects** — old or easily-confused article titles route to the current article while being kept off its visible name (the `{{distinguish}}` case).
- **DNS and email aliasing** — addresses that route a message to the right destination without appearing in the rendered signature or display name.

structural_signature:
**Sig role-phrases:**

- **the concept** — the controlled-vocabulary entry that strings attach to
- **the displayed name set** — the preferred label plus any sanctioned synonyms, the concept's authoritative public face
- **the hidden string inventory** — typos, deprecated descriptors, offensive prior names, phonetic guesses attached to the concept but withheld from display
- **the retrieval index** — the lookup surface that consumes *both* the displayed and hidden sets, so any string in either lands the user on the concept
- **the display layer** — the rendered output that consumes *only* the displayed set, never surfacing a hidden string
- **the width asymmetry** — the index is deliberately wider than the display, the gap between them being exactly the absorbed long tail of how users type
- **the silent-update property** — hidden strings can be added or removed without changing anything the system says back, since they carry no display obligation
- **the per-variant routing call** — each incoming form is sorted to one of three tiers: accept-as-input-only (hidden), accept-and-display (alt), or exclude

what_it_is_not:
- **Not a bug or a stale index.** "The search finds it but the page won't call it that" is the device working, not a broken redirect to be reconciled. A hidden string deliberately grants recognition during search while withholding authority during presentation; the mismatch is the intended consequence of a label sitting on the hidden tier.
- **Not an endorsement of the variant.** Indexing "heart attack" or a retired or offensive prior name as a hidden entry term makes it *findable*, not *sanctioned*. The device accepts the form as input precisely so it need not appear as output; the surfaced label stays the preferred term, so absorbing an embarrassing name for retrieval does not put it on the concept's public face.
- **Not an `altLabel`.** A displayed synonym carries a display obligation — adding it changes what the vocabulary says back to the user — whereas a hidden label carries none and can be added or dropped silently. Treating the two as interchangeable collapses the input surface into the output surface, which is exactly the conflation the third tier exists to undo.
- **Not a redirect that rewrites the display.** Routing a user to the concept is all the hidden string does; it never overrides the rendered name. The display layer consumes only the displayed set, so a hidden label moves *where the user lands*, not *what the result is called*.
- **Not a genuinely cross-substrate pattern.** Product search, wiki redirects, DNS aliases, and MeSH entry terms are not distinct substrates but one — a retrieval index over a catalog — in different wrappers. What recurs across truly different substrates is the more general `markedness` + `aliasing` insight (recognition-canonical and display-canonical coming apart), not the SKOS convention itself.

clarity:
Naming the hidden label makes legible a confusion that looks, from the outside, like a defect: the search bar resolves a term the result page refuses to call itself by. Without the concept, that mismatch reads as a stale index or a broken redirect — something to be reconciled. With it, the same behavior is recognized as a deliberate split between two jobs a vocabulary asks of a string, *recognition during search* and *authority during presentation*, which a single-tier name list silently fuses. Once a vocabulary maintainer holds those apart, the sharper question becomes available: for any given variant — the typo, the retired descriptor, the offensive prior name, the phonetic guess — *should this form be accepted as input, displayed as a sanctioned synonym, or neither?* That is a three-way decision (`hiddenLabel` vs. `altLabel` vs. excluded) that the flat preferred-term-plus-synonyms model cannot even pose.

The distinction also clarifies what an entry term *commits the system to*. An `altLabel` carries a display obligation — adding it changes what the vocabulary says back to the user — whereas a hidden label carries none and can be added or removed silently as misspellings and dialect variants accumulate. Seeing this lets the curator treat the retrieval index as legitimately *wider* than the display layer rather than as an inconsistency to be flattened, and turns the long tail of how users actually type into a maintainable input surface kept cleanly separate from the authoritative output surface — so that absorbing a retired or embarrassing name for findability no longer risks endorsing it as the concept's public face.

manages_complexity:
A controlled vocabulary's recurring headache is the unbounded variety of strings real users send at a concept: every misspelling of "receive," every regional name for a disease, every retired descriptor a decade-old citation still uses, every offensive prior name no curator wants on the result page. Trying to manage that variety on a single flat list of names forces an impossible trade — admit each variant as a displayed synonym and the public face of the concept bloats with typos and slurs, or refuse it and the concept becomes unfindable to anyone who types it the wrong way. The hidden label tames this by splitting the one quantity the maintainer was overloading — "the names of this concept" — into two separately sized surfaces governed by a single per-variant decision. The maintainer no longer reasons about each string against the whole tangle of search-and-display obligations at once; for any incoming variant the question collapses to a three-way routing call read straight off the variant's character: is this form one to *accept as input only* (`hiddenLabel`), to *accept and also display as sanctioned* (`altLabel`), or to *exclude entirely* — and the typo, the dialect form, the embarrassing legacy name each land in the first bin without any further deliberation about how they interact with the presentation layer.

What the maintainer reads off, having made that call, is that the retrieval index is legitimately *wider* than the display layer, and the gap between the two widths is exactly the absorbed long tail. This is the compression: the display surface stays small, clean, and authoritative no matter how many variants accumulate, because every variant added on the hidden tier changes the index width without touching the display width at all — the asymmetric-update property means the maintainer can pour the entire messy input distribution into one surface while the other holds constant. Two qualitative outcomes then follow without case-by-case analysis. A reported "the search finds it but the page won't call it that" mismatch is read not as a stale-index defect to reconcile but as the intended consequence of a string sitting on the hidden tier — recognition without authority, by design. And the *commitment* of any name becomes a one-bit fact: a hidden label binds the system to nothing it says back and can be silently dropped, whereas an `altLabel` binds the public output and cannot. The sprawling "how do we handle every way users type this concept while keeping its public name clean?" reduces to: sort each variant into one of three tiers, let the hidden tier grow freely, and read the two surface widths as deliberately unequal.

abstract_reasoning:
The device's foundational move is a *per-variant routing inference* read off the character of an incoming string. For any form a user might send at a concept — a typo, a deprecated descriptor, an offensive prior name, a phonetic guess, a sanctioned synonym — the curator reasons to one of three placements: *accept as input only* (`hiddenLabel`), *accept and also display as sanctioned* (`altLabel`), or *exclude entirely*. The inference runs from what the variant *is* to what the system should *do* with it: a misspelling or an embarrassing legacy name is findable-but-not-endorsable, so it routes to the hidden tier; a legitimate alternate name is endorsable, so it routes to display; a form the vocabulary refuses to accept at all is excluded. This is a three-way decision the flat preferred-term-plus-synonyms model cannot even pose, and it is licensed by separating the two jobs a name does — *recognition during search* and *authority during presentation* — that a single-tier list silently fuses.

A *diagnostic* move reclassifies an apparent defect. Facing the behaviour "the search bar finds it but the result page won't call it that," the analyst infers not a stale index or a broken redirect to be reconciled but a string deliberately sitting on the hidden tier — recognition without authority, by design. The reasoning runs from the observed search/display mismatch to its intended cause, and it inverts the naive reading: the mismatch is the device working, not failing. This pairs with a *commitment inference* that reduces a name's obligations to one bit: a hidden label binds the system to *nothing it says back* and can therefore be added or removed silently, whereas an `altLabel` binds the public output and cannot be changed without altering what the vocabulary tells the user. From a label's tier the analyst thus predicts whether editing it is a silent maintenance act or a change to the concept's public face.

A *predictive / boundary* move follows from the asymmetry between the two surfaces. Because the retrieval index consumes both the displayed and the hidden sets while the display layer consumes only the displayed set, the index is legitimately *wider* than the display, and the gap between the two widths is exactly the absorbed long tail of how users type. The analyst predicts that pouring more variants onto the hidden tier widens findability while holding the display surface constant — clean and authoritative no matter how many misspellings and dialect forms accumulate — so the move is to read the two surface widths as *deliberately unequal* rather than as an inconsistency to flatten. The boundary on all this is the substrate: the inferences presuppose a retrieval system over a controlled vocabulary or catalog with a separable index and display layer, so the routing, mismatch-diagnosis, commitment, and width-asymmetry reasoning apply to thesauri, product search, wiki redirects, and routing aliases alike, and do not extend to settings lacking the index-versus-display split the device exploits.

knowledge_transfer:
Within information science the hidden label transfers *as mechanism* across every system that runs a retrieval index over a controlled vocabulary or catalog. The three-tier `prefLabel` / `altLabel` / `hiddenLabel` structure, the per-variant routing call, the index-wider-than-display asymmetry, and the silent-update property all carry intact from one such system to the next. In **thesauri and subject vocabularies** it is the canonical use — MeSH entry terms, retired ERIC descriptors, SKOS `hiddenLabel` strings indexed but suppressed from the displayed heading. In **product search** the same device maps misspellings and competitor names onto catalog items without showing them on the product page; in **wiki redirects** old or distinguishable titles route to the current article while being kept off its visible name; in **DNS and email aliasing** addresses route without appearing in signatures. A maintainer who learns the device in one of these reads "the search bar finds it but the page won't call it that" the same way in all of them — as the hidden tier working by design, not a stale-index defect — and applies the same intervention (route the variant to the hidden tier rather than the display tier). The vocabulary and diagnostics travel without translation because these are not distinct substrates but one substrate in different wrappers: *an indexed string that does not appear in the rendered output of the indexed object*.

That observation is exactly why the case **beyond** the home substrate is **(B), shared abstract mechanism rather than the named device travelling.** The hidden label's apparent cross-domain spread (library catalog, e-commerce, wiki, DNS) is not genuine substrate-bridging — every example is the same retrieval-over-catalog structure — so what would carry to a *truly* different substrate (a cognitive, social, or formal system) is not "hidden label" but the more general patterns it instantiates: `markedness` (a canonical form and non-canonical variants, asymmetrically privileged) and `aliasing` (multiple names for one referent), here specialized to the case where some aliases are deliberately *non-displaying*. The portable lesson is that *recognition-canonical and display-canonical can come apart* — the form a system accepts as input need not be the form it endorses as output — and that lesson is available in substrate-neutral terms from those parent primes. The home-bound cargo is the SKOS-specific apparatus: the named third tier, the `skos:hiddenLabel` semantics, the entry-term curation workflow, the treatment of the index as a maintainable input surface distinct from the display surface. Invoking "a hidden label" outside a retrieval index renames the parts and borrows the input-versus-output split while dropping the index column that gives the device its meaning — illuminating by resemblance, but the mechanism that actually recurs across domains is `markedness` + `aliasing`, not the library-science convention (see *Structural Core vs. Domain Accent*).


Secret Sprawl

one_liner: Diagnose a credential's danger not by whether its primary store is locked but by whether its copies have spread into more places than the issuer tracks, so revocability collapses and rotation cost scales with the unknown copy count.

core_idea:
Secret sprawl is the information-security failure state in which a class of access-granting tokens — passwords, API keys, signing credentials, session secrets, certificates, encryption keys — proliferates into more storage locations, configurations, and holders than the issuing authority tracks, causing *revocability to collapse*: the ability to deactivate the token everywhere it can act becomes practically unavailable because the set of copies is not fully enumerable.

The mechanism runs as follows. An access-granting credential is issued for a legitimate purpose and stored in a primary location. From there it spreads — copied into a developer's local environment to test a service integration, embedded in a CI pipeline configuration, pasted into a shared team password manager, checked into a feature branch, included in a container image, forwarded in a Slack message to a colleague, replicated into a disaster-recovery configuration. Each copy is cheap to make and often invisible to the credential's original issuer. The issuing authority's registry records the credential as existing in one or a few known locations; the actual copy count has grown beyond that record without any update to it. The credential now acts everywhere it has been copied, but the issuer cannot enumerate that set. When a rotation or revocation is required — because a security incident has occurred, because a developer has left the organisation, because an audit has flagged the credential's age — "rotate the credential" translates in practice to "locate and update every copy," and that set is partially or wholly unknown. The cost of rotation therefore scales with the number of unknown copies rather than with the number of known ones, making it super-linear in sprawl. In practice, rotations simply do not happen on credentials with high sprawl, and the credential's effective lifetime becomes indefinite.

The diagnostic the failure establishes is a shift from *storage safety* (is the primary location secure?) to *revocation closure* (can we reach every place this token currently acts, in bounded time, if we need to?). The intervention family follows: inventorying the copy graph by treating each token as a node and each custodian as an edge; replacing long-lived credentials with short-lived automatically-rotating tokens whose sprawl is bounded by their lifetime; routing all access through a credential broker that issues fresh tokens at use time so the live-copy set is always known; and preferring capability tokens that name specific permitted actions over ambient credentials that grant broad authority, so partial-graph revocation is still effective.

scope_of_application:
Secret sprawl is at home in information security, but its mechanism lives wherever the *authority-as-artifact-with-a-central-issuer* structure is literally present — a copyable token of revocable authority, an issuer expected to track its holders, and a copy graph that outruns the registry. The habitats below are genuine co-instances of that same machinery, not metaphor; where there is no copyable artifact and no issuer who could call authority back (reputation, soft power), the defining move — revocation — has no analog, and the portable lesson there belongs to the parent `delegation_of_authority` / revocation-closure primes.

- **Information security (home)** — credential, key, and token sprawl: passwords, API keys, signing credentials, certificates, and encryption keys spreading across services, code, CI runners, container images, and operator workstations.
- **Building-key management** — physical key sets and door codes dispersed across holders, recoverable only through re-keyable cores when the holder set is incompletely known.
- **Notarial and seal-based systems** — seals and signing authority tracked by a sealed register plus a recall journal, the copy graph being the clerks and envoys holding impressions.
- **Regulatory licensing and clinical credentialing** — issued entitlements, break-glass EHR accounts, medication-cabinet codes, and just-in-time provisioning across many shift workers.
- **Historical statecraft** — royal seals and signet rings copied to clerks, governors, and envoys, recallable only by physical recovery from an incompletely-known holder set: the identical failure centuries earlier.

structural_signature:
**Sig role-phrases:**

- **the access-granting token** — a copyable carrier of revocable authority (password, API key, signing credential, session secret, certificate, encryption key)
- **the issuing authority** — the party that minted the token and is expected to track where it lives
- **the registry** — the issuer's index of known storage locations, recording only a few of the copies
- **the copy graph** — the structure where each token is a node and each custodian a directed edge; its dangerous property is the token's out-degree
- **the cheap-copy / costly-revoke asymmetry** — the load-bearing imbalance: each copy is trivial to make and often invisible to the issuer, while revocation cost scales with the copy count
- **the silent fan-out** — the uncatalogued propagation events (Slack forward, cloned branch, colleague's laptop, disaster-recovery replica) that no audit trail recorded
- **the revocation closure** — the binding property: can the issuer reach every place the token now acts, in bounded time, to deactivate it?
- **the revocation gap** — the failure: unknown edges in the copy graph make the live set unenumerable, so rotation cost is super-linear in sprawl and the token's effective lifetime drifts toward indefinite
- **the bounding remedies** — the closed menu keyed to how each bounds the copy set: by time (short-lived auto-rotating tokens), by issuance (a broker minting fresh tokens at use), or by scope (capability tokens surviving partial-graph revocation)

what_it_is_not:
- **Not a storage problem.** Secret sprawl is a property of the *copy graph*, not of any one vault: a perfectly secured primary store with an unenumerable set of copies is exposed, and a token with a small, fully-observed graph is safe even if no single store is hardened beyond the rest. The binding question shifts from "is this location locked down?" to "do we know everywhere this token can act, and could we deactivate all of them in a bounded window?"
- **Not a breach or a leak.** Nothing exited a perimeter; the system was *dispersed*, not penetrated. Sprawl is the *pre-leakage* state in which the legitimate holder set multiplied beyond the issuer's index, so the binding constraint is revocation, not exit. The credential acts everywhere it was copied — all of it sanctioned — and the failure is the inability to recall it.
- **Not about the secret's strength or secrecy.** A long, high-entropy, well-guarded credential is fully sprawled the moment its copies outrun the registry; the defect is collapsed *revocability*, not weak confidentiality. Hardening or lengthening the token does nothing for revocation closure — the failure is that authority cannot be withdrawn in bounded time, regardless of how good the secret is.
- **Not rotation cost that scales with known copies.** "Rotate the credential" means "locate and update every copy," so its cost is super-linear in the *unknown* copy count, not the recorded one. This is exactly why the credentials most in need of rotation — the oldest and most widely used — are the hardest to rotate, and why rotations on heavily sprawled secrets quietly never happen.
- **Not escape-and-leakage or a containment failure.** Escape/leakage names the exit *event* of a constrained quantity through an unintended pathway; a containment failure is a breached perimeter. Secret sprawl is neither — the perimeter is intact and nothing escaped; the holder set multiplied *inside* it, making withdrawal rather than exit the failure mode.
- **Not present where there is no copyable artifact and issuer.** Applied to diffuse social influence, reputation, or soft power, "secret sprawl" is metaphor with no mechanism behind it: there is no token to copy and no central issuer who could call authority back, so the defining move — revocation — has no analog. You cannot rotate a reputation; the framing's force depends on a revocable copyable artifact being literally present.

clarity:
Naming secret sprawl converts credential management from a *storage* problem into a *graph* problem, and that reframing is the whole of its clarifying force. Without the label, a security team's mental model of a secret is a single guarded location, and "are we secure?" decomposes into "is that location locked down?" — a question that can be answered yes while the credential silently exists in twenty other places. With the label, each secret becomes a node whose dangerous property is its *out-degree*: the unknown set of copies, each reachable through some custodian edge. The sharp question is no longer "is this storage safe?" but "do we know everywhere this token can act, and could we deactivate all of them in a bounded window?" That is *revocation closure*, and it is a property of the copy graph, not of any one vault.

The reframing also sharpens what an access event means and where the real cost lives. "This person had access" becomes "this person, plus everyone they propagated the secret to, had access" — surfacing the silent fan-outs (the Slack forward, the cloned branch, the colleague's laptop) that no audit trail recorded. And it explains a failure that storage-thinking cannot: why rotation simply does not happen on the most sprawled credentials. Once "rotate" is understood to mean "locate and update every copy," its cost is seen to scale with the *unknown* copy count rather than the known one, so the credentials most in need of rotation are precisely the ones hardest to rotate, and their effective lifetime drifts toward indefinite. Localizing the breakdown to revocation closure — rather than to storage hygiene or attacker capability — is what tells the team that the fix lives in bounding the copy set (short-lived auto-rotating tokens, broker-issued credentials, capability tokens that survive partial-graph revocation), not in hardening any single store.

manages_complexity:
The security posture of a credentialed system, asked about wholesale, is a high-dimensional and open-ended thing: every secret, every store, every holder, every threat actor, every path an attacker might take. "Are we secure?" admits no bounded answer, and a team that tries to enumerate threats faces a list with no natural end. Secret sprawl compresses that whole posture, for the class of access-granting tokens, into a single graph property: *revocation closure* — for each token, is the set of places it can act bounded, named, and reachable for deactivation in a finite window? The question of whether the organization is safe against credential compromise stops being an unbounded threat-enumeration and becomes a property of one structure, the copy graph, in which each secret is a node and each custodian a directed edge. The analyst no longer reasons about attackers at all; they reason about whether a graph is fully observed.

What that collapse lets the team track is the *out-degree* of each token node — the known versus unknown copy set — in place of the diffuse "is this secure." A token whose copy graph is fully enumerable has revocation closure and is safe under recall; a token whose graph has unknown edges does not, and the gap is read directly off the missing edges rather than re-derived from a fresh threat model per credential. The same single property reprices the operations that storage-thinking cannot explain: "this person had access" expands, by following edges, to "this person plus everyone downstream of them," and the cost of rotation is read off the graph as scaling with the unknown copy count — which is why the most sprawled credentials are precisely the ones that never get rotated, their effective lifetime drifting toward indefinite. One graph property predicts the access fan-out, the rotation cost, and the revocation gap together.

The branch structure the compression imposes is what makes the remedy selectable rather than improvised, because it sorts every intervention by *how it bounds the copy set* rather than by which store it hardens. Reading the failure as a copy-graph property forks the fixes cleanly: bound the set by *time* (short-lived auto-rotating tokens, whose sprawl can never exceed their lifetime), bound it by *issuance* (a broker that mints fresh tokens at use so the live set is always known), or survive an unbounded set by *scope* (capability tokens naming specific actions, so partial-graph revocation still cuts off authority). A practitioner therefore stops asking the unanswerable "are we secure?" and asks, per token, a finite and checkable question — is its copy graph enumerable, and if not, which of the three bounding moves applies — turning an open-ended security posture into a single per-node graph property with a small, closed menu of structural fixes keyed to it.

abstract_reasoning:
Secret sprawl licenses a set of reasoning moves in information security, all turning on its central reframing — a secret is a node in a copy graph whose dangerous property is its out-degree (the set of copies, each reachable through a custodian edge) — and on the property that property defines: revocation closure.

The signature *diagnostic* move evaluates a credential by asking whether its copy graph is fully observed rather than whether its primary store is locked down. The reasoner asks, per token, "do we know everywhere this token can act, and could we deactivate all of them in a bounded window?" — and infers exposure directly from the *missing edges*: a token whose copy graph is fully enumerable has revocation closure and is safe under recall; a token with unknown edges does not, and the gap is read off the unobserved part of the graph rather than re-derived from a fresh threat model. Crucially, the reasoner stops reasoning about attackers at all and reasons about whether a graph is fully observed — converting "are we secure?" from an unbounded threat-enumeration into a checkable property of one structure.

A *fan-out / propagation* move follows edges to reprice an access event. "This person had access" is not read literally but expanded along the copy graph to "this person plus everyone downstream of them" — surfacing the silent fan-outs (the Slack forward, the cloned branch, the colleague's laptop, the disaster-recovery replica) that no audit trail recorded. The reasoner infers the true blast radius of a credential from its out-degree, not from the issuer's registry, which records only the few known locations.

A *cost-prediction* move reads rotation difficulty off the same graph and explains a failure storage-thinking cannot. Because "rotate the credential" means in practice "locate and update every copy," its cost scales with the *unknown* copy count rather than the known one — super-linear in sprawl. From this the reasoner predicts the field's characteristic pathology: the credentials most in need of rotation (oldest, most widely used) are precisely the most sprawled and therefore the hardest to rotate, so rotations quietly do not happen and effective credential lifetime drifts toward indefinite. One graph property predicts the access fan-out, the rotation cost, and the revocation gap together.

The *interventionist* move sorts remedies by *how each bounds the copy set* rather than by which store it hardens, yielding a small closed menu keyed to the diagnosis. Bound the set by *time* — short-lived automatically-rotating tokens, whose sprawl can never exceed their lifetime. Bound it by *issuance* — route all access through a credential broker that mints fresh tokens at use time, so the live-copy set is always known. Or survive an unbounded set by *scope* — capability tokens that name specific permitted actions rather than granting ambient authority, so partial-graph revocation still cuts off authority even when the holder set is incompletely known. The reasoner selects among the three by predicting which can restore bounded revocability for the token at hand, and predicts the effect of each (sprawl bounded by lifetime, live set always enumerable, or partial revocation still effective).

Underlying all of these is a *boundary-drawing* move that localizes the failure to revocation closure rather than to storage hygiene or attacker capability — and so insists the fix lives in bounding the copy set, not in hardening any single vault. The reasoner draws the line: a perfectly secured primary store with an unenumerable copy graph is exposed, and a token with a small enumerable graph is safe even if no single store is hardened beyond the rest, because the binding property is whether authority can be *withdrawn* in bounded time.

knowledge_transfer:
Within information security secret sprawl transfers *as mechanism*. The copy-graph reframing (each secret a node whose dangerous property is its out-degree), the revocation-closure diagnostic (do we know everywhere this token can act, and could we deactivate all of them in a bounded window?), the fan-out repricing of an access event, the cost-prediction that rotation scales with the *unknown* copy count (so the most sprawled credentials are the least rotated), and the three-way remedy menu keyed to how each bounds the copy set (time: short-lived auto-rotating tokens; issuance: a credential broker minting fresh tokens at use; scope: capability tokens surviving partial-graph revocation) carry intact across the credential classes that share the substrate — passwords, API keys, signing credentials, session secrets, certificates, encryption keys, across services, code, CI runners, container images, and operator workstations. The transfer is mechanical here because each is the same substrate — an access-granting token cheap to copy but bound to authority that must remain revocable — so the copy-graph property and its bounding moves refer to the same machinery throughout.

Unusually, the mechanical transfer extends *past* information security to any substrate where the same *authority-as-artifact-with-a-central-issuer* structure is literally present — and this is the key to characterizing its reach honestly. Building-key management (re-keyable cores), notarial and seal-based systems (sealed register plus recall journal), regulatory licensing, and clinical credentialing (just-in-time provisioning, break-glass EHR accounts, medication-cabinet codes) all instantiate the same machinery: a copyable token of revocable authority, an issuer expected to track its holders, and a copy graph that outruns the registry. The interventions hardened in cryptographic key management therefore port cleanly to those systems, and the historical-statecraft case (royal seals and signet rings copied to clerks, governors, and envoys, recallable only by physical recovery from an incompletely-known holder set) is the same failure centuries earlier. These are *co-instances of one domain-specific pattern*, not independent prime-level applications — which is precisely why the breadth does not lift secret sprawl to a prime: its extra-domain instances are re-descriptions of the same authority-token machinery rather than new substrates exercising a substrate-independent signature.

Where that machinery is *absent*, the transfer fails, and this marks the honest boundary. In non-token substrates — diffuse social influence, reputation, soft power — there is no copyable artifact and no central issuer who could call authority back, so the defining move, *revocation*, has no analog: you cannot rotate a reputation. So invoking "secret sprawl" there would be case (A) metaphor with no mechanism behind it. What genuinely travels at that level of abstraction is the *parent* pattern the failure instantiates — a *revocable right bound to a copyable carrier whose copying outpaces the issuer's index*, best carried by existing primes `delegation_of_authority` (authority granted along paths that go uncatalogued), `escape_and_leakage` and `containment` (the perimeter is intact but the holder set multiplied inside it), and a candidate *revocation-closure* prime (for any granted right, can the authority reach every place it now acts, in bounded time?) that would graft onto delegation-of-authority and is broader than secret sprawl. What stays home-bound is everything token-specific: the credential taxonomy, rotation and broker mechanics, capability tokens, and the copy-graph custodian edges are access-control machinery that presupposes an artifact and an issuer. So the honest cross-domain lesson is to carry the parent (delegation of authority / revocation closure / the authority-as-artifact framing) only where a revocable copyable artifact with an issuer is actually present; "secret sprawl," as named, carries access-control-substrate baggage, and its structural force depends on that framing being literally present — which is exactly why it is a sharp domain-specific specialization rather than a prime. (See *Structural Core vs. Domain Accent*.)