Normative Assumption Explicitness

batch: First Iteration/Batch 42

slug: normativity

slug: transparency

slug: consent

components:
Normative Claim
Value Source
Affected Party Map
Competing Norm
Decision Rationale
Fact–Value Boundary
Contestation Channel
Assumption Record
Review Trigger
Legitimacy Check
Value Conflict Matrix
Scope Condition

mechanisms:
Value Audit
Ethical Impact Assessment
Metric Value Review
Policy Rationale Statement
Stakeholder Deliberation
Decision Record
Ethics Checklist
Model Card Value Section
Values Statement

essence_one_line: Make value judgments and ought-claims explicit before treating them as neutral facts.

compression_statement: When a decision, metric, model, policy, or design appears objective while depending on unstated judgments about what ought to matter, Normative Assumption Explicitness identifies the normative claims, names their sources, maps affected parties, compares competing norms, documents rationale, and defines how those assumptions can be contested or revisited.

core_intuition: Many systems smuggle values into technical choices, metrics, eligibility rules, or default priorities. The problem is not that values exist; action always depends on some standard of what is better, worse, acceptable, fair, safe, or legitimate. The problem is that hidden values cannot be evaluated, contested, or improved. This archetype works by moving value premises from the background into a structured record that people can inspect and govern.

canonical_formula: apparently_neutral_decision + hidden_ought_claims -> normative_claim_inventory + value_sources + affected_party_map + competing_norms + decision_rationale + contestation_and_review

structural_problem: A policy, model, metric, design, or decision is framed as objective, technical, natural, inevitable, or merely evidence-based while relying on unstated value judgments about what should count, which outcomes matter, whose interests matter, what risks are acceptable, and which tradeoffs are legitimate.

condition: A decision is presented as value-neutral.

example: A ranking system is described as objective even though it weights speed over accessibility and precision over recall.

condition: A metric or target defines success.

example: A department optimizes response time without stating whether speed should outrank thoroughness, equity, or user comprehension.

condition: A rule affects rights, burdens, eligibility, or opportunity.

example: A benefits policy uses eligibility criteria that quietly privilege some life situations over others.

condition: Stakeholders disagree but the disagreement is disguised as factual or technical.

example: One group calls a threshold “scientifically correct” while another experiences it as unfair because the value tradeoff is not named.

condition: A model or process automates prioritization.

example: A triage system orders cases by predicted benefit but does not state the ethical assumptions behind the prediction target.

condition: A governance decision needs public or organizational legitimacy.

example: Leaders choose a controversial policy and need to explain the values and alternatives rather than only announcing the rule.

symptom: Objectivity theater

diagnostic_meaning: The decision uses technical language to avoid acknowledging value choices.

symptom: Metric moralization

diagnostic_meaning: A metric is treated as if it directly defines what is good or successful.

symptom: Unexplained priority

diagnostic_meaning: One value, such as efficiency or safety, repeatedly wins without being explicitly chosen or justified.

symptom: Stakeholder surprise

diagnostic_meaning: Affected people discover the value tradeoff only after the decision has already constrained them.

symptom: Factual debate masking value conflict

diagnostic_meaning: Participants argue over data because they lack a shared way to discuss the underlying ought-claim.

symptom: Rationale gap

diagnostic_meaning: The organization can state what it decided but not why that value priority was legitimate in context.

root_tension: Action requires normative standards, but unstated standards look like facts. The system needs enough value commitment to decide, but enough value transparency to let affected parties understand, contest, and revise the decision.

not_applicable_when: The disagreement is purely empirical after values are already explicit.

reason: Use evidence review, hypothesis testing, causal mechanism mapping, or disconfirming evidence protocols instead.

not_applicable_when: The task is to design the fairness of participation, notice, impartiality, or appeal.

reason: Use Procedural Fairness Design unless the central missing move is naming hidden value premises.

not_applicable_when: The problem is choosing weights among known objectives in an optimization model.

reason: Use or review Objective Weighting Governance when the distinctive challenge is legitimate weighting and sensitivity of weights.

not_applicable_when: The task is to align actions with an already accepted purpose.

reason: Use Purpose Alignment Design when the purpose is known and the gap is means-end drift, not hidden normativity.

not_applicable_when: The task is a generic moral debate with no decision, policy, model, metric, or design to govern.

reason: This archetype is operational; it does not promote philosophical debate as an archetype.

structural_intervention: Convert implicit value premises into explicit, sourced, contestable, and revisable decision assumptions. The intervention separates empirical claims from normative claims, identifies affected parties, compares credible competing norms, records the accepted rationale, and defines when the assumption should be reopened.

step: Locate the apparently neutral decision surface.

logic: Identify the policy, metric, model, rule, design choice, or evaluation frame that currently appears technical or factual.

step: Extract the normative claims.

logic: Ask what the decision assumes should matter, what should be optimized, what risk is acceptable, who should be protected, and what tradeoff is being accepted.

step: Separate fact claims from value claims.

logic: Clarify which statements are empirical and which statements choose standards, priorities, duties, thresholds, or acceptable harms.

step: Name value sources and affected parties.

logic: Record where each value premise comes from and who benefits, bears risk, is constrained, or needs voice in contestation.

step: Compare competing norms.

logic: Identify reasonable alternatives and what each would preserve, sacrifice, or redistribute.

step: Document rationale and scope.

logic: State which value priority is accepted for this context, why, under what limits, and with what uncertainty or dissent.

step: Create contestation and review conditions.

logic: Define who can challenge the assumption, what evidence or stakeholder change would trigger review, and how future revisions will be recorded.

rule: If a decision changes who benefits or bears risk, treat it as normatively loaded until shown otherwise.

rationale: Distributional effects usually imply value choices even when phrased as efficiency or performance.

rule: If a metric defines success, name the value embedded in the metric.

rationale: Measurement design selects what will be seen, rewarded, and optimized.

rule: If two values conflict, do not hide the conflict inside language of objectivity.

rationale: Legitimate disagreement is easier to govern when the competing norms are explicit.

rule: If affected parties cannot see or contest the assumption, transparency is incomplete.

rationale: Normative assumptions need governance, not only disclosure.

rule: If a value source is weak, informal, or contested, lower confidence in the decision rationale or add review.

rationale: A value can be substantively plausible and still need legitimacy support.

invariant: Fact–value distinction

description: Empirical evidence should inform normative choices, but it should not be used to disguise them.

invariant: Affected-party visibility

description: People who bear consequences must not disappear from the rationale.

invariant: Competing norm visibility

description: The selected value should be visible alongside credible alternatives it displaces.

invariant: Contestability

description: An explicit assumption should remain open to challenge through defined channels.

invariant: Scope discipline

description: A value premise accepted in one context should not be silently generalized to all contexts.

invariant: Revision memory

description: Rationale, dissent, and review conditions should be recorded so later decisions can learn from earlier ones.

outcome: More honest decision rationale

description: Decision makers can explain not only what they chose but what value standard made the choice acceptable.

outcome: Reduced false neutrality

description: Technical, metric, or evidence language no longer hides the underlying ought-claim.

outcome: Better stakeholder trust and contestation

description: Affected parties can understand and challenge the assumptions shaping outcomes.

outcome: Improved governance of models and metrics

description: Evaluation targets, thresholds, and scoring choices become reviewable as value-laden design decisions.

outcome: More durable policies

description: Policies are easier to revise because the original value premises and review triggers are visible.

prose_essence:
Normative Assumption Explicitness makes hidden value judgments visible before they harden into policy, metrics, models, rankings, thresholds, or designs. The archetype does not ask teams to pretend values are absent. It asks them to stop treating value choices as if they were neutral facts.

The core move is simple: identify what the decision assumes *ought* to matter, where that standard comes from, who is affected by it, what competing norms were possible, why the selected standard is acceptable in this context, and when it should be reopened.

prose_when_to_use:
Use this archetype when a decision claims neutrality while allocating benefit, burden, risk, opportunity, credibility, attention, or protection. It is especially useful when a metric defines success, a model automates prioritization, a policy creates eligibility, or a design choice makes one value easy and another value costly.

It is also useful when people seem to be arguing about facts but are actually disagreeing about standards. For example, a team may debate whether a threshold is “accurate enough,” while the deeper question is whether false positives or false negatives are more acceptable for the people affected.

prose_structural_problem:
The structural problem is false neutrality. A system presents a decision as technical, objective, scientific, efficient, or evidence-based, but the decision depends on unstated assumptions about what should count, what should be optimized, what risks are acceptable, and whose outcomes matter.

Hidden normative assumptions create several predictable harms. They prevent affected parties from understanding why a decision was made. They shift value conflict into technical language. They make contestation look irrational because the real ought-claim is never named. They also make later revision difficult because no one knows which value premise the original decision depended on.

prose_intervention_logic:
The intervention begins by locating the decision surface: a metric, model, rule, ranking, threshold, policy, budget, product choice, or evaluation frame. The next step is to extract the normative claims embedded in that surface. What is being treated as success? What risk is tolerated? What duty is being prioritized? What kind of fairness is assumed? What is being sacrificed?

The archetype then separates empirical claims from value claims. Evidence can show what is likely, but it does not by itself decide what should matter. Once that boundary is visible, the intervention names value sources, maps affected parties, compares competing norms, records the accepted rationale, and defines contestation and review conditions.

The output is not merely a statement of values. It is a governed assumption record: what value premise guided action, why it was accepted, what alternatives were considered, who is affected, where dissent remains, and when the premise should be reopened.

prose_key_components:
### Normative Claim

A normative claim is the ought-claim hidden inside the decision. It may say that safety should outrank autonomy, that efficiency should outrank customization, that accuracy should outrank explainability, or that consistency should outrank contextual adjustment. Naming this claim prevents it from disguising itself as technical necessity.

### Value Source

A value source explains where the standard comes from. It may come from law, policy, mission, professional duty, community expectation, user preference, safety culture, or informal organizational norms. A value source does not automatically settle the issue, but it makes the claim’s authority and legitimacy inspectable.

### Affected Party Map

An affected party map identifies who benefits, who bears risk, who is constrained, and who needs a way to contest the assumption. This component keeps values from being defined only by those with technical or institutional power.

### Competing Norm

A competing norm is a credible alternative standard. For example, a school assessment might value test performance, learning growth, inclusion, wellbeing, or long-term capability. The intervention is weak if it only states the winning value. It becomes useful when it also shows what alternatives were displaced.

### Decision Rationale

The decision rationale records why a particular normative assumption was accepted in context. It should explain the chosen value priority, the rejected alternatives, the scope of the choice, and the conditions that would justify revision.

### Fact–Value Boundary

The fact–value boundary separates empirical claims from value claims. A risk model may estimate likelihood; a policy still decides which risks are acceptable. A performance metric may measure speed; a governance decision still decides whether speed should count as success.

### Contestation Channel

A contestation channel gives affected parties or reviewers a way to challenge the assumption. Without this channel, explicit values can become a polished explanation for a predetermined choice.

### Assumption Record

The assumption record preserves the claims, sources, affected parties, alternatives, rationale, and review triggers. It is the memory of the intervention and supports future audit, learning, and accountability.

### Review Trigger

A review trigger defines when the normative assumption should be reopened. Triggers may include new harms, stakeholder change, legal change, metric drift, deployment in a new context, or evidence that the chosen value priority is producing unacceptable effects.

prose_common_mechanisms:
### Value Audit

A value audit reviews a decision surface to find hidden priorities and displaced alternatives. It implements the archetype by moving implied standards into an explicit record.

### Ethical Impact Assessment

An ethical impact assessment can surface harms, duties, rights, and value conflicts. It is not the archetype by itself; it is a structured assessment mechanism that supports explicit normative reasoning.

### Metric Value Review

A metric value review asks what a metric rewards, ignores, normalizes, or sacrifices. This mechanism is especially important because metrics often appear objective while quietly defining what should count as success.

### Policy Rationale Statement

A policy rationale statement records the values and tradeoffs behind a rule. It implements the archetype when it names affected parties, value sources, competing norms, and review conditions rather than merely defending the rule.

### Stakeholder Deliberation

Stakeholder deliberation creates a forum for affected parties and decision makers to compare values. It is a mechanism, not the archetype. It matters because normative assumptions often look different from the position of those who bear consequences.

### Decision Record

A decision record makes value premises durable. It stores what was assumed, what alternatives were rejected, what dissent remains, and when the assumption should be revisited.

### Ethics Checklist

An ethics checklist can prompt useful questions, but it should not be confused with the archetype. A checklist implements this archetype only when the answers are concrete, sourced, contestable, and tied to the actual decision.

### Model Card Value Section

A model card value section documents the value assumptions embedded in a technical system. It can identify intended uses, excluded uses, target choices, evaluation priorities, affected groups, and review triggers.

### Values Statement

A values statement is an artifact that can support the archetype, but it is not sufficient. “We value fairness” does not make a decision governable until the specific fairness assumption, tradeoff, rationale, and review path are stated.

prose_failure_modes:
**Ethics theater** occurs when the organization fills out a checklist or publishes values without changing actual decision rationale. Mitigation: link every value claim to a concrete decision, affected-party implication, competing norm, and review trigger.

**Hidden priority remains hidden** occurs when a record lists many values but refuses to say which value wins in conflict. Mitigation: require comparison of competing norms and document the accepted priority for the current context.

**False consensus** occurs when one value source is treated as universal. Mitigation: record dissent, map affected parties, and provide contestation channels.

**Over-philosophizing** occurs when the process turns into abstract moral debate. Mitigation: anchor the discussion in a decision surface: a rule, metric, model, threshold, policy, or design choice.

**Paralysis from infinite contestation** occurs when no closure rule exists. Mitigation: pair this archetype with a regress termination rule or decision closure criterion.

**Expert capture** occurs when technical or legal experts define the values without affected-party knowledge. Mitigation: combine with epistemic inclusion design, lived experience capture, or stakeholder deliberation.

prose_neighbor_distinctions:
**Procedural Fairness Design** designs fair process: voice, notice, impartiality, reasons, and appeal. Normative Assumption Explicitness names the value premises that guide the decision. They often combine but are not the same.

**Objective Weighting Governance** governs how objectives are weighted in optimization or scoring systems. Normative Assumption Explicitness is broader and includes nonnumeric decisions, policies, metrics, and designs.

**Stakeholder Mapping and Engagement** identifies and engages affected parties. This archetype uses stakeholder understanding to expose and govern hidden ought-claims.

**Goal Congruence Alignment** aligns behavior with accepted goals. Normative Assumption Explicitness asks whether the goals or standards themselves contain hidden values.

**Epistemic Inclusion Design** protects fairness in knowledge production. Normative Assumption Explicitness protects transparency of value premises. Epistemic inclusion may be necessary when excluded knowers can reveal hidden assumptions.

**Purpose Alignment Design** aligns means with an accepted purpose. Use Normative Assumption Explicitness when the purpose or the values inside that purpose are hidden or contested.

prose_cross_domain_examples:
In AI hiring, a screening tool may define success using a label that rewards prior corporate experience. The archetype asks the team to state the value assumption, compare it with alternatives such as potential and equity, map affected candidates, and revise the rationale or label.

In public health, a vaccination policy may be described as purely scientific. The evidence matters, but the policy also contains assumptions about liberty, collective protection, trust, and acceptable risk. The archetype separates those layers.

In product design, a platform may optimize engagement and call the choice user-centered. Normative explicitness asks whether engagement should be treated as wellbeing, autonomy, revenue, habit formation, or something else.

In education, a school ranking may reward test scores while ignoring learning growth, inclusion, or wellbeing. The archetype makes the values embedded in ranking criteria visible.

In organizational promotion, a process may claim meritocracy while rewarding visibility, hours, self-promotion, or risk-taking. Normative explicitness reveals what “merit” actually means in the system.

prose_non_examples:
A generic ethics seminar is not this archetype unless it changes how a concrete decision records and governs value assumptions.

A values poster is not this archetype. It becomes relevant only when those values are linked to decisions, tradeoffs, affected parties, and review conditions.

A purely statistical confidence interval is not this archetype. It expresses uncertainty, not what outcome should matter.

A stakeholder list without value analysis is not this archetype. It can support the work, but the intervention requires surfacing and governing ought-claims.

prose_tradeoffs:
The main tradeoff is speed. Normative explicitness takes time, especially when affected parties must be included. The solution is proportionality: use lightweight records for low-stakes decisions and deeper review for high-stakes or contested decisions.

Another tradeoff is contestability versus closure. The goal is not endless debate. The archetype should define how assumptions can be challenged and when enough review has occurred for action.

A third tradeoff is transparency versus strategic ambiguity. Sometimes ambiguity helps a coalition move forward, but hidden values can later produce mistrust when consequences become visible.

A fourth tradeoff is breadth of participation. Wider participation reveals more values and harms, but it requires representation choices, facilitation, and decision rules.

prose_invariants_to_preserve:
The first invariant is the fact–value distinction. Evidence can guide a decision, but evidence does not erase the need to say what should matter.

The second invariant is affected-party visibility. The people who bear risk or burden should not disappear from the value rationale.

The third invariant is competing norm visibility. A chosen value should be visible alongside the credible alternatives it displaces.

The fourth invariant is contestability. Explicit assumptions should be open to challenge through defined channels.

The fifth invariant is scope discipline. A value assumption accepted in one context should not silently govern every context.

prose_target_outcomes:
A successful use of the archetype produces more honest decision rationale. Decision makers can explain not only what they chose but what standard made the choice acceptable.

It reduces false neutrality. Technical language no longer hides the underlying value choices.

It improves governance of models, metrics, and policies by making targets, thresholds, rules, and priorities reviewable.

It improves stakeholder trust where disagreement is inevitable, because people can see and contest the assumptions shaping outcomes.

It also improves revision. When a decision later fails, reviewers can examine the value premise rather than guessing what the original choice meant.

prose_parameter_dimensions:
**Explicitness depth** determines how much detail is required. A low-stakes decision may need only a brief note. A high-stakes automated decision may need a full assumption record, stakeholder review, alternatives analysis, and formal review triggers.

**Participation scope** determines who helps surface and contest assumptions. Internal review may be enough for low-risk work. Public policy, healthcare triage, AI screening, or eligibility systems often require affected-party input.

**Rationale formality** determines whether the output is a lightweight note, decision record, policy rationale, audit report, or formal documentation. More durable or repeated decisions need more durable rationale.

**Norm conflict granularity** determines how carefully competing values are compared. Some decisions need a short list of alternatives; others need a matrix that shows how options affect autonomy, safety, equity, efficiency, privacy, dignity, and accountability.

**Review cadence** determines whether the assumption is reviewed once, periodically, or when triggered by harm reports, drift, new stakeholders, or context change.

prose_variants_and_near_names:
**Metric Value Review** is a variant for metrics, rankings, KPIs, benchmarks, and scores. It asks what the metric values and what it hides.

**Policy Rationale Explicitness** is a governance variant for rules and administrative decisions. It states why a policy’s value priority is legitimate and revisable.

**Algorithmic Value Assumption Audit** is a technical-system variant for AI, scoring, ranking, and automation. It surfaces value choices in labels, targets, thresholds, loss functions, and evaluation priorities.

**Objective Weighting Disclosure** is a merge-review variant because Objective Weighting Governance may deserve separate treatment in optimization-heavy settings.

Near names include value assumption audit, hidden value audit, normative assumption audit, ought-claim disclosure, and value premise transparency. Ethics checklists and values statements should point here as mechanisms or artifacts, not as separate archetypes.

Metric Value Review

short_definition: A metric-focused variant that asks which values are embedded in what is measured, optimized, rewarded, or declared successful.

distinctive_feature: The normative assumption is encoded in measurement design rather than in an overt policy statement.

distinct_from_parent: The parent can apply to any decision or design; this variant focuses on metrics as the carrier of value assumptions.

still_same_parent_because: The intervention still makes ought-claims visible by naming what the metric values, what it ignores, and why that priority is legitimate or contestable.

use_when: A key decision is being governed by a metric, score, ranking, KPI, threshold, benchmark, or performance target.

use_when: Stakeholders treat the metric as neutral even though the metric selects what should count as success.

use_when: Metric gaming, Goodhart-style distortion, fairness concerns, or missing dimensions are plausible.

domain: AI evaluation

example: A team documents why false positives are weighted differently from false negatives instead of presenting the score as purely technical.

domain: education

example: A school reviews whether graduation-rate optimization is displacing learning quality or support for harder-to-serve students.

failure_modes: Treating every missing value as a metric defect rather than recognizing that some values require deliberation, not measurement.

failure_modes: Adding many values to a dashboard without deciding how conflicts among them will be governed.

Policy Rationale Explicitness

short_definition: A governance-focused variant that requires policies to state the value premises and balancing judgments behind their rules.

distinctive_feature: The normative assumption appears as a rule justification and must be made legible for governance, appeal, and revision.

distinct_from_parent: The parent applies broadly; this variant emphasizes formal rules, public reasons, administrative standards, and decision records.

still_same_parent_because: The core work remains identifying ought-claims, value sources, affected parties, competing norms, and rationale.

use_when: A policy affects people who need to understand why the rule exists and what values it protects.

use_when: The policy could be seen as arbitrary, biased, opaque, or merely bureaucratic if its normative rationale is hidden.

use_when: Rule exceptions, appeals, or public legitimacy depend on knowing what the policy is trying to preserve.

domain: Platform governance

example: A content policy states the values behind takedown rules and the conditions under which safety overrides expression.

domain: Workplace policy

example: A remote-work policy states which fairness, coordination, inclusion, and autonomy assumptions justify the rule.

failure_modes: Publishing polished rationale after the real decision has already been made without allowing contestation.

Algorithmic Value Assumption Audit

short_definition: A technical-system variant that surfaces the normative choices embedded in datasets, labels, targets, thresholds, loss functions, deployment constraints, and evaluation priorities.

distinctive_feature: The value assumptions are embedded in technical artifacts that may look like engineering choices rather than governance choices.

distinct_from_parent: The parent is technology-neutral; this variant names the recurring AI/data-system pathway by which normative choices become hidden.

still_same_parent_because: The intervention still identifies normative claims, value sources, affected parties, competing norms, and rationale before treating the system as neutral.

use_when: A model, ranking system, scoring system, or automation workflow is presented as objective because it is technical.

use_when: Design choices allocate benefits, burdens, false-positive risks, false-negative risks, or opportunities across groups.

use_when: Documentation needs to explain not only performance but also the values encoded in performance criteria.

domain: Hiring tools

example: A vendor documents why one success label was chosen over another and how the choice affects candidates with nontraditional backgrounds.

domain: Healthcare triage

example: A triage model states why urgency, resource scarcity, equity, and survival probability are balanced in a particular way.

failure_modes: Reducing the audit to technical bias metrics while leaving the underlying value tradeoff unstated.

Objective Weighting Disclosure

short_definition: A weighting-focused variant that discloses how competing objectives are prioritized when a decision procedure combines them.

distinctive_feature: The decisive normative move is numerical or ordinal weighting among objectives.

distinct_from_parent: The parent covers hidden value assumptions generally; this variant targets the special case where values are encoded as weights.

still_same_parent_because: The core intervention is still value transparency and contestability, but the weight-specific form may require its own governance later.

use_when: A score, optimization model, prioritization rule, or ranking depends on weights among competing objectives.

use_when: The chosen weights determine who benefits, who bears risk, or which outcome is treated as more important.

use_when: Stakeholders may incorrectly assume that the weights are dictated by math rather than value judgment.

domain: Grant scoring

example: A committee explains why community impact is weighted more heavily than publication output for a specific funding stream.

failure_modes: Treating numerical weights as precise moral truth rather than as a contestable governance choice.

near_names:
Value Assumption Audit
Hidden Value Audit
Normative Assumption Audit
Ought-Claim Disclosure
Ethics Checklist
Values Statement


Normed Encounter Surface Design

type: archetype

source: generated

batch: Batch 03

canonical_family: Context Binding & Cue Capture

aliases:
Governed Encounter Surface
Accessible Contact Commons
First-Contact Surface Design

slug: interface

slug: containerization

slug: context

slug: boundary

slug: observability

slug: coordination

slug: trust

slug: feedback

essence_one_line: Create a durable, accessible, mutually observable, and norm-governed layer where unfamiliar participants can safely come into contact.

compression_statement: When strangers, roles, or groups need opportunities for contact, but encounters are absent, inaccessible, private, fleeting, or normless, allowing isolation, domination, avoidance, and conflict to persist., build and steward a shared contact layer with accessible entry, mutual observability, low-stakes interaction affordances, visible norms, privacy and exit boundaries, conflict repair, and evidence-driven maintenance.

core_intuition: The intervention works by making the hidden structure explicit, binding action to reviewable evidence, and preserving a controlled response when assumptions fail.

canonical_formula: normed_encounter_surface_design = scope + evidence + governed transformation + validation + feedback + recovery

structural_problem: Strangers, roles, or groups need opportunities for contact, but encounters are absent, inaccessible, private, fleeting, or normless, allowing isolation, domination, avoidance, and conflict to persist.

trigger_conditions: The target condition recurs across cases or domains rather than appearing as a one-off exception.

trigger_conditions: Local workarounds do not expose the underlying structural relation.

trigger_conditions: Consequential decisions depend on an assumption that is not yet explicit or validated.

trigger_conditions: The system needs a reusable intervention with monitoring, exception handling, and revision.

symptoms: Results depend strongly on hidden configuration or context.

symptoms: Teams use the same label for materially different structures.

symptoms: Failures are discovered only after downstream use.

symptoms: Evidence, ownership, or update rules are fragmented.

root_tension: The system needs enough structure to act consistently without freezing uncertain assumptions into permanent truth.

anti_signatures: Do not use when a single concrete mechanism fully solves the problem without broader governance.

anti_signatures: Do not use when an accepted neighbor already contains the same source prime, components, and decision logic.

anti_signatures: Do not use as a metaphor when the stated trigger conditions and invariants are absent.

structural_intervention: Build and steward a shared contact layer with accessible entry, mutual observability, low-stakes interaction affordances, visible norms, privacy and exit boundaries, conflict repair, and evidence-driven maintenance.

action_logic: Define the governed object, scope, and unacceptable outcomes.

action_logic: Make the relevant structure, evidence, and assumptions explicit.

action_logic: Select components and mechanisms appropriate to domain and risk.

action_logic: Apply the transformation in bounded, observable form.

action_logic: Validate outcomes, inspect exceptions, and revise or reverse when evidence fails.

decision_rules: Use stronger validation and independent review as consequence severity increases.

decision_rules: Do not promote an implementation artifact to the archetype level.

decision_rules: Stop or redesign when target invariants are violated.

decision_rules: Refresh the intervention when inputs, context, or downstream use changes.

target_invariants:
explicit scope
traceable evidence
bounded authority
visible uncertainty
reviewable exceptions
recovery or revision path

expected_outcomes:
better structural fit
earlier failure detection
less hidden drift
more accountable decisions
safer transfer across domains

Physical Civic Encounter Surface

variant_type: subtype

short_definition: A Normed Encounter Surface Design subtype distinguished by contact occurs in a shared place.

distinctive_feature: contact occurs in a shared place

distinct_from_parent: This subtype foregrounds contact occurs in a shared place.

still_same_parent_because: It uses the same structural components, decision rules, and outcome invariants as the parent.

use_when: The case is distinguished by contact occurs in a shared place.

use_when: The parent intervention structure still applies.

example: Apply Physical Civic Encounter Surface where contact occurs in a shared place.

failure_modes: Treating the subtype label as sufficient without the parent controls.

Digital Commons Encounter Surface

short_definition: A Normed Encounter Surface Design subtype distinguished by contact occurs through a governed online layer.

distinct_from_parent: This subtype foregrounds contact occurs through a governed online layer.

use_when: The case is distinguished by contact occurs through a governed online layer.

example: Apply Digital Commons Encounter Surface where contact occurs through a governed online layer.

Temporary Event Encounter Surface

short_definition: A Normed Encounter Surface Design subtype distinguished by a recurring event supplies the encounter layer.

distinct_from_parent: This subtype foregrounds a recurring event supplies the encounter layer.

use_when: The case is distinguished by a recurring event supplies the encounter layer.

example: Apply Temporary Event Encounter Surface where a recurring event supplies the encounter layer.


Novelty-Driven Attention Capture

batch: Second Iteration/Batch 06

aliases: Attention Reorientation by Pattern Violation

alias_records: Attention Reorientation by Pattern Violation

source_primes: attention emphasis

slug: attention

slug: emphasis

slug: contrast

slug: signaling

slug: framing

slug: representation

slug: threshold

slug: priming

slug: hierarchy

components:
Attention Target Definition
Expectation Baseline Model
Controlled Pattern Violation
Attention Routing Target
Novelty Intensity Threshold
Habituation and Saturation Monitor
Baseline Restoration Boundary
Semantic Bridge
Accessibility and Load Filter
False Alarm Control
Novelty Budget
Consent and Manipulation Guardrail

mechanisms:
Contrastive Visual Interrupt
Semantic Incongruity Hook
Rhythm Break or Pause
Threshold-Triggered Anomaly Highlight
Discrepant Event Demonstration
Novelty Cadence Rotation
Surprise-to-Action Bridge

essence_one_line: Make a target noticeable by breaking a stable pattern just enough to trigger attention, then connect that break to the intended meaning or action before the novelty becomes noise.

compression_statement: Novelty-Driven Attention Capture uses controlled expectation violation as an attention intervention: identify the baseline pattern, choose a departure channel, calibrate its intensity and frequency, ensure the departure is meaningful rather than random, route the captured attention to a target interpretation or action, and monitor for habituation, confusion, manipulation, or overload.

core_intuition: Attention is drawn to differences that matter against a background of expectations. A sudden contrast, dissonance, pause, unexpected example, or visual rupture works only when the audience has a baseline to depart from and when the departure resolves into something worth noticing.

canonical_formula: attention_capture = meaningful_pattern_violation × target_relevance × baseline_legibility - (noise + habituation + manipulation_risk)

structural_problem: A message, signal, element, state change, or concept needs attention, but the audience or operator is habituated to a stable pattern, overloaded by competing cues, or treating the current context as routine; ordinary emphasis is either ignored, drowned out, or normalized as background noise.

trigger_conditions: A repeated layout, rhythm, message genre, workflow, lesson format, or status display has become predictable enough that users skim or ignore it.

trigger_conditions: A target element is important but does not naturally stand out against its context.

trigger_conditions: Existing emphasis methods have become saturated because too many things are highlighted at once.

trigger_conditions: A decision, warning, transition, concept, or opportunity needs reorientation of attention before interpretation can occur.

trigger_conditions: The design context permits a controlled pattern departure without violating safety, accessibility, consent, or trust.

trigger_conditions: The audience can recognize both the baseline pattern and the meaning of the departure.

trigger_conditions: The organization needs a repeatable way to use novelty without turning every communication into noise or shock.

symptoms: Users miss important changes because the screen, message, soundscape, or workflow always looks the same.

symptoms: Every item is highlighted, producing emphasis inflation and no real attentional priority.

symptoms: A presentation, lesson, or campaign is technically correct but forgettable because it never violates the expected pattern.

symptoms: Alarms, notifications, or status changes are ignored because novelty cues are too frequent or too weakly tied to true importance.

symptoms: Teams add louder colors, bigger labels, or more urgent wording without improving noticing or action.

symptoms: An unexpected cue grabs attention but leaves the audience confused about what to do next.

symptoms: The same novelty tactic loses effect after repeated exposure.

root_tension: Attention needs discontinuity to reorient, but too much discontinuity destroys trust, comprehension, accessibility, and the background stability that makes novelty work in the first place.

anti_signatures: The audience has not yet learned a stable baseline pattern, so there is no expectation to violate.

anti_signatures: The main task is to allocate existing attention across many demands rather than create capture for one target.

anti_signatures: The target can be made clear through ordinary hierarchy, labeling, or focal emphasis without a pattern break.

anti_signatures: The context is safety-critical and the proposed novelty cue could startle, distract, or create alarm fatigue without formal human-factors review.

anti_signatures: The proposed surprise is manipulative, deceptive, stigmatizing, traumatic, or unrelated to the target meaning.

anti_signatures: The problem is measurement of attention decay or priming, not generation of a capture cue.

anti_signatures: A stable, low-arousal, predictable experience is the goal and interruptions would undermine user trust or performance.

structural_intervention: Create a bounded attention-capture event by modeling the current expectation baseline, selecting a meaningful departure from that baseline, calibrating departure intensity and frequency, connecting the departure to the intended target, and restoring a stable background so the cue remains rare enough to matter.

action_logic: Identify the attention target: the element, signal, idea, state change, transition, or action that deserves notice.

action_logic: Describe the current baseline pattern that makes the target easy to miss, such as visual uniformity, semantic routine, cadence, genre convention, workflow repetition, or alert saturation.

action_logic: Choose a departure channel: visual contrast, spatial rupture, semantic incongruity, temporal pause, rhythmic disruption, interaction change, unusual example, or threshold-triggered anomaly display.

action_logic: Calibrate the departure so it is strong enough to be noticed but not so strong that it overwhelms the target or undermines trust.

action_logic: Add a routing bridge that tells the audience why the departure matters and what interpretation, decision, or action should follow.

action_logic: Limit frequency and scope so novelty remains rare against a stable background.

action_logic: Test for accessibility, cognitive load, emotional safety, deception risk, and domain-specific safety constraints.

action_logic: Monitor whether the cue continues to produce noticing, comprehension, and action, or whether habituation and saturation require reset or redesign.

action_logic: Retire, rotate, or restore novelty cues once they no longer serve the target.

decision_rules: Use novelty only when the target is important enough to justify attention interruption.

decision_rules: The baseline must be legible; if users cannot recognize what is normal, the departure will read as random noise.

decision_rules: The departure must be meaning-linked; a surprising cue without a semantic or action bridge is not this archetype.

decision_rules: Reserve high-intensity novelty for high-stakes or rare events; use lower-intensity breaks for teaching, presentation, or persuasion contexts.

decision_rules: Do not stack multiple novelty cues unless the design intentionally sequences attention from one target to another.

decision_rules: If users begin ignoring the cue, reduce frequency, strengthen threshold discipline, or redesign the baseline rather than escalating intensity indefinitely.

decision_rules: When the cue affects vulnerable groups, safety, consent, or high-stakes decisions, use conservative intensity and human review.

target_invariants: The target of attention remains explicit and proportionate to the importance of the cue.

target_invariants: The baseline remains stable enough that a departure is detectable.

target_invariants: The novelty cue remains interpretable, accessible, and meaning-linked.

target_invariants: The cue does not use deception, humiliation, trauma activation, or coercive manipulation as its attention engine.

target_invariants: The design protects against alert fatigue, overload, and habituation.

target_invariants: The captured attention is routed to comprehension or action rather than left as mere sensation.

expected_outcomes: Higher likelihood that the intended audience notices the target element, message, transition, or state change.

expected_outcomes: Improved differentiation of important signals from routine background patterns.

expected_outcomes: More memorable explanations, demonstrations, or campaign moments when novelty resolves into meaning.

expected_outcomes: Reduced emphasis inflation because novelty is reserved for bounded targets rather than applied everywhere.

expected_outcomes: Clearer transition from attention capture to interpretation, decision, or action.

expected_outcomes: Better management of habituation, alarm fatigue, and novelty wear-out.

Visual Pattern-Break Attention Capture

short_definition: Use a controlled visual departure from an established layout, color, scale, rhythm, or grouping pattern to make a target element immediately noticeable.

distinctive_feature: The attention cue is created by violating a visible baseline pattern rather than by simply enlarging or coloring the target.

distinct_from_parent: The parent may use visual, semantic, temporal, social, or procedural novelty; this variant is specifically visual and perceptual.

still_same_parent_because: It still creates attention through controlled expectation violation and must route that attention to an intended meaning or action.

use_when: A visual field has become uniform, repetitive, or crowded enough that ordinary emphasis is ignored.

use_when: The target element is important enough to justify interrupting the baseline pattern.

use_when: The departure can be made legible without harming accessibility or comprehension.

example_domains:
interface design
presentation design
safety signage
museum exhibit design

domain: dashboard design

example: A risk tile breaks the regular grid and uses a distinct shape only when an operating threshold is crossed.

cause: Too many elements are made novel at once.

mitigation: Maintain a novelty budget and restore baseline patterns after attention is routed.

Semantic Incongruity Attention Capture

short_definition: Introduce a meaningful but unexpected word, analogy, example, contradiction, or frame shift that interrupts semantic monotony and invites interpretation.

distinctive_feature: The pattern break occurs at the level of meaning rather than physical layout or timing.

distinct_from_parent: The parent covers all novelty routes; this variant uses semantic incongruity as the primary capture channel.

still_same_parent_because: The operative structure is still expectation baseline, controlled violation, attention capture, interpretation routing, and habituation control.

use_when: A message, lesson, campaign, or explanation is likely to be skimmed or treated as routine.

use_when: A controlled incongruity can be resolved into the intended meaning rather than causing confusion.

use_when: The communicator can avoid stigmatizing, deceptive, or manipulative surprise.

example_domains:
advertising
teaching
public communication
writing and editing

example: A physics lesson begins with a counterintuitive demonstration that conflicts with a learner's expectation, then resolves it through the target concept.

cause: The incongruity is memorable but not connected to the intended concept.

mitigation: Require a semantic bridge from the surprise to the target interpretation.

Temporal Rhythm-Break Attention Capture

short_definition: Break an established cadence, sequence, pause pattern, or auditory rhythm so attention reorients to the next signal.

distinctive_feature: The attention cue is a timing departure rather than a static visual or semantic departure.

distinct_from_parent: The parent is channel-agnostic; this variant uses rhythm, pause, timing, or sequence as the attention channel.

still_same_parent_because: The same baseline-violation-routing-habituation structure governs its use.

use_when: A sequence, performance, briefing, service flow, or notification stream has become predictable enough to fade into the background.

use_when: The interruption can be brief and meaningful rather than arbitrary.

use_when: The system can avoid unsafe startle, fatigue, or interruption overload.

example_domains:
music
presentations
operations alerts
service design

domain: music

example: A dissonant chord in a consonant passage marks the transition into a structurally important section.

cause: Rhythm breaks become too abrupt, frequent, or consequentially ambiguous.

mitigation: Calibrate intensity and reserve breaks for target-worthy moments.

Anomaly Salience for Critical Signals

short_definition: Reserve novelty cues for abnormal or high-priority states so critical signals stand out against a stable operating baseline.

distinctive_feature: Novelty is governed by operating thresholds and alarm-fatigue constraints.

distinct_from_parent: The parent is domain-general; this variant is focused on critical-signal monitoring and escalation.

still_same_parent_because: It still captures attention by making a verified departure from the expected pattern visible.

use_when: A monitoring environment contains many routine signals and a few high-stakes departures.

use_when: Operators must notice the departure quickly without being trained to ignore constant alarms.

use_when: The novelty cue can be tied to real thresholds and verified states.

example_domains:
industrial operations
health monitoring
cybersecurity
incident response

domain: operations dashboard

example: A normally uniform status grid breaks shape and position only for verified incidents, not every minor fluctuation.

cause: Frequent false positives teach users that the novelty cue is not meaningful.

mitigation: Tie the cue to calibrated thresholds and review false positives.

near_names:
Pattern-Break Attention Hook
Novelty Cue Design
Unexpected Contrast Emphasis
Monotony-Break Attention Design
Surprise Cue Attention Design


Null Finding Warrant Calibration

batch: Batch 07

aliases:
Absence-of-Evidence Guardrail
Evidence-of-Absence Calibration
Null Finding Interpretation
Negative Evidence Calibration
Non-Detection Inference
Detection-Power Null Inference
Search Sensitivity Warranting
Informative Absence Assessment
Null Result Warrant Review

slug: evidence

slug: uncertainty

slug: probability

slug: screening

slug: falsifiability

slug: completeness

components:
Target Presence Claim
Null Finding Record
Search Scope Boundary
Detection Power Model
Observation Condition Record
Non-Detection Alternative Set
Absence Warrant Grade
Follow-Up or Stopping Rule
Minimum Detectable Presence Threshold
Upper-Bound Statement
Coverage Gap Register
Cost of Missed Detection Profile
Confidence and Caveat Label

mechanisms:
Null Finding Warrant Memo
Search Sensitivity Matrix
Detection Power Checklist
Negative Test Interpretation Protocol
Likelihood Ratio for Non-Detection
Minimum Detectable Presence Table
Coverage Map and Blind-Spot Review
Silent Monitor Assurance Review

essence_one_line: Treat a failure to find something as evidence of absence only after calibrating whether the search would probably have detected it if it were present.

compression_statement: When a search, test, monitor, investigation, or observation returns nothing, separate the bare null finding from its evidential force. Define the target claim, reconstruct the search scope and detection conditions, estimate the probability of detection if the target were present, adjust for noise, timing, coverage, base rates, and failure modes, then label the null as silence, weak counter-evidence, strong counter-evidence, an upper bound, or a trigger for better search.

core_intuition: Absence is not automatically informative. An empty net tells you something only if the net covered the right water, had holes small enough for the fish, was used at the right time, and would likely have caught the fish if it was there. The archetype makes that hidden detection question explicit so teams do not convert poor visibility into false reassurance or treat a well-powered null finding as meaningless silence.

canonical_formula: null_finding + calibrated_detection_power + search_scope + noise_context -> warranted_absence_claim | silence_label | follow_up_search

structural_problem: A person or system observes no trace of a target condition and must decide what that non-observation means. Without a detection-power calibration, the same phrase—nothing was found—can be overread as proof of absence or underread as useless silence, even though its warrant depends on the search design, observation channel, timing, sensitivity, specificity, and coverage.

condition: A search, test, monitor, sensor, audit, review, interview, survey, or investigation finds no target.

notes: The output may be a negative diagnostic test, no alert, no observed species, no discovered document, no failure reproduction, no complaint, or no statistically significant result.

condition: The practical decision depends on whether the target is probably absent, merely unseen, or still plausible.

notes: Examples include ruling out disease, closing an incident, declaring no defect, accepting a hypothesis, or deciding whether a risk is negligible.

condition: Search sensitivity, scope, or timing is imperfect or contested.

notes: The target may be rare, intermittent, hidden, decayed, small, adversarial, poorly measured, outside the sampled region, or masked by noise.

condition: The audience is tempted to equate no evidence with evidence of no problem.

notes: The archetype is especially important when null findings will be used to justify assurance, dismissal, certification, resource withdrawal, or public communication.

condition: A negative result is being compared across searches with different detection capacities.

notes: The same null result has different meaning when produced by a sensitive, well-scoped search versus a cursory or misdirected one.

symptoms: A report says “no evidence was found” without stating where, how, when, and with what sensitivity the search was conducted.

symptoms: A negative result is treated as proof even though the test, sensor, or sample had low power or poor coverage.

symptoms: Teams continue searching indefinitely because they have no rule for when non-detection becomes informative enough to act on.

symptoms: A null result is dismissed as meaningless despite a search design that made the target very likely to be detected if present.

symptoms: Decision-makers confuse absence of reports, complaints, alerts, failures, or records with absence of harm, demand, compromise, or risk.

symptoms: A claim is insulated from falsification because every failed search is explained away after the fact.

symptoms: A system has negative evidence but no recorded warrant level, so later reviewers cannot tell whether the absence claim was justified.

root_tension: The decision needs a usable conclusion from non-observation, but non-observation has evidential force only when the observation process was capable of revealing the target under relevant conditions.

anti_signatures: The target was directly and continuously observed to be absent with negligible detection uncertainty; ordinary observation recordkeeping may suffice.

anti_signatures: The task is designing sample size and power before a formal hypothesis test; use hypothesis_test_power_calibration as the primary archetype.

anti_signatures: The task is general claim evaluation with null and alternative hypotheses, not specifically interpreting a null finding; use hypothesis_testing_frame.

anti_signatures: The task is updating beliefs from a mix of positive and negative evidence using a full probabilistic model; use bayesian_belief_updating as the primary frame.

anti_signatures: The task is searching for missing cases to complete a coverage map rather than interpreting the evidential force of not finding them; use completeness_audit.

anti_signatures: The target is a weak positive signal rather than a non-detection; use weak_signal_triage.

anti_signatures: The absence is primarily an aesthetic, rhetorical, or spatial design element rather than evidence; use negative space or silence-as-signal patterns instead.

structural_intervention: Build a null-finding warrant frame: name the target claim, record the null finding, map the search scope and observation pathway, estimate detection power under target-present conditions, adjust for alternative non-detection explanations, assign a warrant grade to the absence claim, and define whether action, follow-up search, monitoring, or communication is justified.

step: State the target-present claim.

logic: Define exactly what would have to exist, occur, be true, or leave a trace for the search to be considered capable of detecting it.

step: Record the null finding without interpretation.

logic: Write the bare observation first: what was not seen, measured, recovered, reproduced, reported, or triggered.

step: Map search scope and observation pathway.

logic: Specify locations, populations, channels, windows, instruments, sensors, sources, archives, logs, witnesses, or sampling frames actually covered.

step: Estimate detection power conditional on presence.

logic: Ask how likely the search would have been to reveal the target if the target were present in the relevant form and regime.

step: List alternative causes of non-detection.

logic: Consider inadequate scope, timing mismatch, intermittent occurrence, masking, sensor failure, reporting barriers, selection bias, adversarial concealment, record decay, and threshold settings.

step: Classify evidential warrant.

logic: Translate detection calibration into a label such as silence, low-warrant absence, moderate counter-evidence, strong evidence of absence, exclusion, upper bound, or unresolved.

step: Attach a caveated conclusion.

logic: Communicate the claim as “not detected under these conditions” or “unlikely above this bound,” rather than as unqualified absence unless the warrant supports it.

step: Define the next search or stopping rule.

logic: If the null is underpowered, improve scope or sensitivity; if sufficiently powered, use it to update belief, close the issue, or set a monitoring cadence.

decision_rules: Use this archetype whenever a negative result will influence belief, assurance, dismissal, certification, escalation, or resource allocation.

decision_rules: Do not upgrade a null finding to evidence of absence unless detection power under target-present conditions is known or credibly bounded.

decision_rules: When detection power is low or unknown, communicate the result as absence of evidence and specify the missing observation capacity.

decision_rules: When detection power is high and search scope matches the target claim, treat the null as evidence against presence, with an explicit confidence or bound.

decision_rules: When target presence would be intermittent, localized, strategic, hidden, or timing-dependent, require a temporal and spatial coverage check before interpreting non-detection.

decision_rules: When consequences of missed detection are severe, use a more conservative warrant threshold and favor follow-up search or monitoring.

decision_rules: When a conclusion depends on no complaints, no alerts, no reports, or no records, audit reporting channels and incentive barriers before inferring absence.

target_invariants: The distinction between “not found” and “not present” remains explicit.

target_invariants: The target claim, search scope, and detection assumptions travel with the absence conclusion.

target_invariants: Detection power is estimated relative to the relevant target state, not to an easier surrogate.

target_invariants: Caveats are not stripped away when the conclusion is summarized downstream.

target_invariants: A high-consequence absence claim cannot rest on an unscoped or low-sensitivity search.

target_invariants: Null findings are allowed to become strong evidence when the search genuinely made the target likely to be seen.

expected_outcomes: Fewer false reassurances from poor visibility, missing reports, weak tests, or sparse monitoring.

expected_outcomes: More useful negative results, because well-powered non-detections are recognized as informative rather than ignored.

expected_outcomes: Clearer stop/continue decisions for investigation, monitoring, testing, or field search.

expected_outcomes: Improved auditability of claims such as “ruled out,” “not observed,” “no evidence,” “unlikely,” and “not detected above threshold.”

expected_outcomes: Better alignment between uncertainty communication and actual search sensitivity.

expected_outcomes: Reduced motivated misuse of absence claims in safety, science, governance, diagnostics, and public communication.

Negative Diagnostic Test Interpretation

short_definition: Interpret a negative diagnostic or screening result using sensitivity, timing, specimen quality, and prior risk.

distinctive_feature: The null finding is a test result with known or estimable sensitivity and specificity.

distinct_from_parent: The parent covers all non-detections; this variant focuses on diagnostic and screening tests.

still_same_parent_because: It still converts non-detection into warrant only through detection-power calibration.

use_when: A medical, safety, or quality test returns negative.

use_when: The result may be used to rule out a condition.

use_when: Test sensitivity varies by timing, sample, subgroup, or threshold.

domain: medicine

example: A negative rapid test is treated as low-warrant early after exposure but higher-warrant after an appropriate testing window.

failure_modes: Treating a low-sensitivity negative result as a rule-out.

failure_modes: Ignoring high prior risk when communicating reassurance.

Silent Monitor Assurance

short_definition: Treat the absence of alerts, logs, incidents, or complaints as reassuring only after checking monitoring coverage and failure modes.

distinctive_feature: The null evidence is produced by a monitoring system whose own observability can fail.

distinct_from_parent: The parent covers any non-detection; this variant emphasizes monitoring infrastructure and alerting pathways.

still_same_parent_because: It still asks whether target presence would likely have produced a detectable signal.

use_when: Dashboards, logs, sensors, complaint channels, or alarms are quiet.

use_when: Silence may be used to close an incident or certify normal operation.

use_when: Monitoring may have blind spots, broken ingestion, thresholds, or reporting barriers.

example_domains: cybersecurity operations public_safety

domain: cybersecurity

example: No alert is not accepted as no compromise until logging coverage and alert threshold integrity are checked.

failure_modes: Mistaking broken monitoring for normal conditions.

failure_modes: Treating low complaint volume as low harm despite reporting friction.

Archival Silence Warranting

short_definition: Use missing documents, records, mentions, or testimony as evidence only after checking record-production and preservation expectations.

distinctive_feature: Detection power depends on social record production, preservation, indexing, and access rather than sensors or tests.

distinct_from_parent: The parent is domain-general; this variant specializes the search-sensitivity question to archives and records.

still_same_parent_because: Archival silence is just a form of non-detection with historically contingent detection power.

use_when: Historical, legal, journalistic, or institutional claims rely on absent records.

use_when: The archive may be incomplete, biased, destroyed, censored, or poorly indexed.

use_when: The expected target would normally leave a durable trace.

example_domains: history law journalism

domain: history

example: No surviving letters mention an event; the historian checks whether such letters were likely to exist and survive.

failure_modes: Equating missing records with non-occurrence.

failure_modes: Ignoring archival destruction or biased preservation.

Field Survey Non-Detection

short_definition: Interpret failure to observe a species, hazard, artifact, or condition in the field through survey effort and detectability.

distinctive_feature: The search has spatial, temporal, environmental, and observer-effort dimensions.

distinct_from_parent: The parent covers all null findings; this variant is tailored to field sampling and survey design.

still_same_parent_because: Non-detection becomes evidence only by estimating detection probability under target presence.

use_when: Detectability depends on season, weather, terrain, observer skill, sampling effort, or target behavior.

use_when: The result may guide conservation, safety, land use, or resource allocation.

domain: ecology

example: A no-sighting survey is reported with effort, timing, and detection probability rather than as simple absence.

failure_modes: Surveying at the wrong season and declaring absence.

failure_modes: Ignoring observer effects or habitat coverage gaps.

Null-Result Assurance Claim

short_definition: Communicate a null finding in assurance language that preserves scope, sensitivity, and caveats.

distinctive_feature: The main intervention is preserving calibrated caveats through communication.

distinct_from_parent: The parent includes reasoning and action; this variant focuses on downstream statement design.

still_same_parent_because: The communication is valid only because the underlying absence warrant was calibrated.

use_when: A public, legal, safety, or executive summary must say what a negative search means.

use_when: Overconfident “no evidence” language could mislead.

use_when: The conclusion needs a bounded statement such as “not detected above X under Y conditions.”

domain: audit

example: The report says “no material finding was detected in the sampled transactions,” not “there was no issue.”

failure_modes: Caveat laundering in executive summaries.

failure_modes: Using technical uncertainty language to obscure rather than clarify.

near_names:
Absence-of-Evidence Guardrail
Evidence-of-Absence Calibration
Negative Evidence Calibration
Detection-Power Null Inference
Non-Detection Warrant Review


Object-Centered Feature Binding

batch: Batch 06

slug: identifiability

slug: relation

slug: synchronization

slug: bijectivity

slug: coupling

slug: classification

slug: observability

slug: correlation

slug: closure

slug: continuity

slug: boundary

slug: composition

slug: chunking

components:
Channel-Specific Feature Token
Feature Channel Inventory
Object Hypothesis Set
Co-Occurrence Context Window
Binding Evidence Vector
One-to-One Assignment Constraint
Binding Conflict Detector
Ambiguity Hold State
Resegmentation or Split Trigger
Learned Association Prior
Active Disambiguation Probe
Binding Confidence Threshold
Binding Rationale Record

mechanisms:
Feature Binding Matrix
Temporal Coincidence Detector
Object File Tracker
Multimodal Fusion Tracker
Assignment Optimization Matcher
Scene Segmentation Pipeline
Active Probe Sequence
Merge/Split Review Queue
Binding Error Audit

essence_one_line: Bind separately detected features to the right object, event, entity, or record by using shared context, co-occurrence cues, exclusivity constraints, and explicit ambiguity states instead of fusing channels blindly.

compression_statement: When parallel channels provide partial features that no single channel can assign safely, create object-centered hypotheses, gather binding evidence across timing, space, continuity, relation, provenance, and prior association, enforce one-to-one or many-to-one constraints where appropriate, preserve ambiguity when cues conflict, and use active disambiguation or split/merge correction before downstream recognition, action, trust, or memory depends on the bundle.

core_intuition: A red patch, a motion vector, a sound, a name, a location, and a timestamp do not automatically belong to the same thing. Binding is the disciplined act of deciding which features travel together as one object or event, while refusing to pretend that coincidence is certainty. Good binding makes a downstream system see the right unit; bad binding manufactures a false unit or tears a real one apart.

canonical_formula: Bind(F_i, C_j, H_k, R, U) -> B, where F_i are channel-specific feature tokens, C_j are context/co-occurrence cues, H_k are object or entity hypotheses, R is the assignment and conflict rule set, U is uncertainty, and B is a set of scoped feature-to-object bindings, split states, or ambiguity holds.

structural_problem: A system receives features through separate processing channels or records. Each channel may be locally accurate, but none carries enough information to say which features belong to the same object, event, entity, or episode. If the system fuses too aggressively, it creates illusory objects, false records, mistaken identities, or unsafe actions. If it refuses to bind, it cannot recognize, act, remember, attribute, or reason about coherent units.

trigger_conditions: Multiple features, signals, attributes, records, or observations arrive through different channels or at different processing stages.

trigger_conditions: At least two candidate objects, events, entities, or episodes could explain the same observed features.

trigger_conditions: Downstream action depends on the correct bundle, not merely on the presence of each feature in isolation.

trigger_conditions: Features can be misassigned because of simultaneity, proximity, occlusion, latency, crowding, missing identifiers, source ambiguity, or noise.

trigger_conditions: There are usable binding cues such as co-occurrence, continuity, common fate, spatial boundary, provenance, recurrence, learned association, or active probe results.

trigger_conditions: False binding and missed binding both have meaningful cost.

symptoms: A system reports a coherent object or event that no single source actually observed.

symptoms: Features from adjacent objects, people, records, or episodes are fused into one false entity.

symptoms: One real object is split into several partial identities because its features arrived through different channels.

symptoms: Small latency or registration errors produce large recognition or attribution mistakes.

symptoms: Operators must manually ask which alarm, camera view, sensor trace, ticket, or patient record belongs with which other signal.

symptoms: A classifier recognizes patterns correctly only after features have already been assigned to the wrong unit.

symptoms: Downstream audits can show the features used, but not why those features were bound together.

root_tension: Binding must be decisive enough to construct usable objects and events, yet conservative enough to avoid treating co-occurrence, proximity, or familiarity as proof of shared identity. The system needs a coherent unit for recognition and action, but coherence is precisely what is uncertain.

anti_signatures: The problem is only to make a visual layout easier to group; use gestalt_grouping_design.

anti_signatures: The relation between already represented entities must be documented; use relation_mapping.

anti_signatures: Conflicting records of the same known entity drifted apart after earlier alignment; use reconciliation_after_drift.

anti_signatures: The task is to authenticate an identity or origin claim with evidence; use evidence_bound_authentication.

anti_signatures: The task is to classify a fully assembled object or pattern; use cascaded_hierarchical_recognition or pattern_detection_with_validation.

anti_signatures: There is only one possible object or entity, so no binding ambiguity exists.

anti_signatures: The channel fusion rule is a fixed engineering implementation with no reusable decision pattern or ambiguity management.

structural_intervention: Create an explicit binding layer between feature extraction and downstream recognition/action. This layer represents candidate object or entity hypotheses, scores cross-channel binding evidence, enforces assignment constraints, carries unresolved ambiguity, triggers probes or resegmentation when cues conflict, and records why the final bundle was accepted, split, merged, or withheld.

action_logic: Inventory the channels and feature tokens before allowing them to collapse into a single object representation.

action_logic: Define candidate object, event, entity, or record hypotheses that features may attach to.

action_logic: Choose binding cues appropriate to the domain: timing, phase, spatial boundary, continuity, common fate, provenance, learned association, identifier evidence, or active interaction.

action_logic: Set the co-occurrence window and assignment constraints before scoring candidates.

action_logic: Score feature-to-hypothesis bindings and compare alternatives rather than accepting the first plausible pairing.

action_logic: Detect conflicts such as many features competing for one slot, one feature fitting several objects, impossible combinations, or evidence that violates continuity.

action_logic: Carry ambiguity forward explicitly when evidence is insufficient, rather than fabricating a crisp object.

action_logic: Probe, reobserve, resynchronize, split, or merge when downstream stakes justify more evidence.

action_logic: Record the binding rationale and revisit it when new evidence arrives or downstream errors reveal misbinding.

decision_rules: Bind only when evidence exceeds the domain-specific threshold for the intended downstream use.

decision_rules: Prefer reversible or ambiguity-preserving outputs when false binding is more costly than delayed action.

decision_rules: Use stronger exclusivity constraints when features are known to be one-to-one, and softer constraints when features can legitimately be shared.

decision_rules: Separate feature detection confidence from binding confidence; a feature can be real yet attached to the wrong object.

decision_rules: Treat synchronization and proximity as cues, not as proof, unless the domain validates them as reliable.

decision_rules: Escalate to active disambiguation when competing bindings would lead to different consequential actions.

decision_rules: Split a bundle when later evidence shows internal contradiction; merge bundles only when shared identity evidence outweighs collision risk.

target_invariants: Preserve channel-specific evidence until binding confidence is sufficient.

target_invariants: Keep the distinction between detected feature, candidate object, and accepted bundle.

target_invariants: Maintain uncertainty and alternative hypotheses where cues do not decide.

target_invariants: Prevent silent many-to-one or one-to-many collisions when the domain requires bijective matching.

target_invariants: Retain enough provenance or rationale to audit a binding decision.

target_invariants: Allow late correction, split, or merge when new evidence invalidates the original binding.

expected_outcomes: Fewer false object/event/entity bundles.

expected_outcomes: Better downstream classification, recognition, attribution, and action selection.

expected_outcomes: Reduced channel-fusion errors caused by latency, crowding, occlusion, identifier collision, or coincidence.

expected_outcomes: More auditable entity and event construction.

expected_outcomes: Clearer handoff between perception, evidence, recognition, authentication, and reconciliation layers.

expected_outcomes: Safer operation under ambiguity because the system can hold, probe, or route uncertain bindings instead of guessing.

prose_tradeoffs: Early binding feels efficient because it creates clean objects quickly. It can also create clean mistakes. Late binding protects against false fusion but may delay action. Strong learned priors help in familiar environments and harm in novel or biased environments. Active probes reduce ambiguity, but they cost time and may create new privacy or safety burdens. A good design makes these tradeoffs explicit instead of burying them inside a model or workflow.

Temporal-Coincidence Binding

short_definition: Binds features to the same object or event when their onsets, offsets, phase, rhythm, or co-change patterns are sufficiently synchronized.

distinctive_feature: Temporal alignment is the primary binding cue rather than semantic, spatial, provenance, or continuity evidence.

distinct_from_parent: The parent can use many cues; this variant emphasizes timing, phase, onset/offset, and co-change windows.

still_same_parent_because: It still solves the same structural problem: assigning separately represented features to the correct object, event, or entity bundle.

use_when: Multiple features or signals are detected in separate channels at nearly the same time.

use_when: The main evidence for belonging-together is simultaneity, shared phase, common fate, or correlated change.

use_when: The system must avoid falsely binding unrelated events that merely happen close together.

domain: vision

example: Color and motion signals that begin and stop together are provisionally bound to the same moving object.

example: Audio and vibration spikes inside the same time window are treated as one machine event until contradicted.

Spatial-Grouping Feature Binding

short_definition: Binds features to object hypotheses using proximity, enclosure, figure-ground separation, shared boundary, or spatial continuity cues.

distinctive_feature: Spatial organization and segmentation cues are the dominant evidence for feature assignment.

distinct_from_parent: The parent covers any binding evidence; this variant centers on spatial and layout cues.

still_same_parent_because: The decision still assigns independently available features to a coherent object or entity representation.

use_when: The candidate objects occupy a represented space, layout, field, map, image, scene, or interface.

use_when: Features can be grouped by neighborhood, boundary, enclosure, continuity, or figure-ground assignment.

use_when: Binding errors arise from clutter, overlap, occlusion, crowding, or misleading layout.

example: Labels are visually bound to the correct controls through proximity and enclosure.

domain: robotics

example: Depth, color, and edge features are grouped into object candidates before manipulation.

Identity-Record Feature Binding

short_definition: Binds attributes arriving through separate records, channels, or observations to the same real-world entity without collapsing distinct entities.

distinctive_feature: The binding object is a record or entity identity rather than a perceptual object in a scene.

distinct_from_parent: It adapts the binding logic to data quality, identifiers, provenance, and merge/split decisions.

still_same_parent_because: It still has the same channel-to-entity assignment structure, only in records rather than perception.

use_when: Attributes or observations arrive from different systems, sessions, sources, or time windows.

use_when: The task is to decide whether they belong to one entity, two entities, or an unresolved ambiguity state.

use_when: Over-merge and under-merge both create downstream harm.

example: Lab results, medication lists, and address changes are linked to a patient only when identity evidence supports the binding.

domain: libraries

example: Publications with similar names are held ambiguous until author attributes can be safely bound.

near_names:
Feature Binding
Object-Feature Binding
Cross-Channel Feature Binding
Co-Occurrence Binding
Perceptual Binding
Multimodal Feature Binding
Entity Feature Assignment


Objective Boundary Governance

batch: Batch 13

canonical_family: Staged Processes & Drift

slug: constraint

slug: feedback

slug: accountability

slug: prioritization

slug: minimalism

slug: boundedness

slug: governance

slug: threshold

slug: adaptation

slug: controllability

slug: postponement

slug: commitment

slug: externality

components:
Charter Baseline
Objective Boundary Ledger
Sub-objective Admission Rule
Removal and Sunset Path
Friction Symmetry Check
Objective Dilution Signal
Trajectory Owner
Re-charter Gate
Opportunity-Cost Register
Deferred Objective Parking Lot
Exception Budget
Baseline Comparison Cadence

mechanisms:
Objective Charter
Objective Change-Control Board
Sub-objective Decision Record
Plus/Minus Boundary Review
Objective Drift Dashboard
Sunset Clause
Re-charter Workshop
Deferred Objectives Backlog
Opportunity-Cost Review
Mission-Creep Audit

essence_one_line: Prevent an objective from silently expanding by making sub-objective additions accountable to the original boundary, opportunity cost, and explicit re-charter rules.

compression_statement: When an actor begins with a bounded objective but accumulates locally reasonable additions that expand the effective goal beyond what the original plan, capacity, legitimacy, or measurement system can support, govern the objective boundary explicitly: preserve the original charter, log additions and removals, require symmetric friction for adding and subtracting sub-objectives, expose opportunity cost and dilution, assign an owner of the trajectory, and use reset or re-charter decisions before the expanded objective becomes the new unexamined baseline.

core_intuition: Objective creep is rarely caused by one obviously bad addition. It happens because each added sub-objective can be defended locally, while nobody owns the cumulative displacement of the original goal. The archetype works by changing the comparison point from “is this addition reasonable?” to “what has the effective objective become, what must now be removed or re-chartered, and who is accountable for the trajectory?”

canonical_formula: ObjectiveBoundaryGovernance = CharterBaseline × AdditionRule × RemovalPath × OpportunityCostTrace × DriftSignal × RecharterGate − (SilentAccretion + Dilution + UnownedTrajectory)

structural_problem: An objective that was originally sized for a particular plan, capacity envelope, mandate, or legitimacy basis expands through incremental sub-objectives. Additions are easy because each one appears helpful, urgent, politically attractive, or naturally adjacent; removals are hard because subtracting a newly added objective creates disappointment, sunk-cost pressure, reputational loss, or coordination cost. The effective goal boundary ratchets outward while the formal objective may remain unchanged, producing diluted purpose, overloaded execution, unclear priority, and drift from the original basis of consent or resource allocation.

trigger_conditions: A bounded objective, mission, product goal, policy aim, research question, or project charter exists, but the boundary is not actively governed after work begins.

trigger_conditions: Participants can add sub-objectives, requirements, success criteria, exceptions, beneficiaries, use cases, safeguards, or adjacent ambitions with lower friction than they can remove them.

trigger_conditions: Each addition has a plausible local rationale, but the cumulative set no longer fits the original time, capacity, budget, legitimacy, measurement, or attention envelope.

trigger_conditions: The baseline for evaluating new additions drifts from the original charter to the already-expanded current state.

trigger_conditions: No role is accountable for the trajectory of the objective boundary as distinct from delivery of the latest accepted additions.

trigger_conditions: Political, reputational, commercial, safety, or identity pressures make subtracting objectives harder than adding them.

trigger_conditions: A fast path admits new goals, while a slow path is required to remove or reset them.

symptoms: The team can explain every added objective but can no longer state the core objective in a stable sentence.

symptoms: Meetings repeatedly add “while we are at it” goals, but removals require escalation, renegotiation, or social conflict.

symptoms: Original success criteria are still listed, yet new criteria quietly dominate behavior and measurement.

symptoms: Plans, budgets, dashboards, or roadmaps grow to include more constituencies, scenarios, or requirements than the original mandate was sized to serve.

symptoms: Delivery slows because the objective has become a bundle of loosely related goals rather than a coherent outcome.

symptoms: Tradeoffs are described as temporary compromises, but the expanded objective is treated as the new baseline at the next review.

symptoms: A project, policy, or product says it is “just adapting” even though the direction of adaptation is almost always additive.

symptoms: The actor protects every stakeholder-requested addition but cannot articulate which original aim is being diluted.

root_tension: The archetype manages the tension between adaptive learning and objective integrity. A goal must be able to learn from new evidence and legitimate opportunities, but the system must not let every locally plausible addition become a one-way ratchet that changes what the actor is actually pursuing without explicit choice.

anti_signatures: The main problem is initial definition of the target being optimized, not post-commitment accretion; use objective_function_alignment.

anti_signatures: The main problem is weighting already-acknowledged competing objectives, not silent addition of new ones; use objective_weighting_governance.

anti_signatures: The main problem is local units optimizing against whole-system goals; use goal_congruence_alignment.

anti_signatures: The main problem is defining the system-of-interest boundary for analysis or responsibility; use system_scope_definition.

anti_signatures: The main problem is capacity promises exceeding available resources without goal-boundary drift; use overcommitment_prevention.

anti_signatures: The main problem is feature or work perimeter expansion rather than objective dilution; consider scope_creep_containment or a future scope-creep-specific archetype.

anti_signatures: The objective truly changed because the environment, mandate, ethics, or evidence changed; re-charter rather than treating adaptation as creep.

anti_signatures: The additions are reversible experiments with explicit stop rules, capacity accounting, and learning intent; use scoped_experimentation or stage_gate_process as neighbors.

structural_intervention: Create an objective-boundary governance loop that preserves the original charter as a reference state, logs changes to the effective objective, requires explicit admission criteria for sub-objectives, pairs every addition with opportunity-cost and removal consideration, tracks dilution and ratchet direction, assigns an accountable boundary owner, and triggers reset, pruning, postponement, or formal re-chartering when additions change the objective beyond its original envelope.

action_logic: Name the original objective, its protected outcome, its legitimacy basis, and the plan or resource envelope it was sized for.

action_logic: Separate core objective, supporting sub-objectives, optional enhancements, and excluded-but-tempting adjacent aims.

action_logic: Create an objective boundary ledger that records additions, removals, substitutions, deferred goals, rationale, cost, owner, and expected effect on the original objective.

action_logic: Require every proposed addition to pass an admission rule: fit to charter, measurable contribution, opportunity cost, capacity impact, reversibility, stakeholder effect, and removal candidate.

action_logic: Make subtraction operationally possible by defining removal, sunset, rollback, and postponement paths before additions are accepted.

action_logic: Monitor boundary drift using indicators such as objective count, exception count, stakeholder class expansion, budget/time expansion, priority dilution, decision-cycle load, and original-outcome attention share.

action_logic: Apply symmetric friction: additions should not be easier than removals unless a deliberate emergency or safety exception is invoked.

action_logic: Escalate to a reset or re-charter gate when accumulated changes alter the objective, not merely the task list.

action_logic: Communicate the decision: prune, defer, re-rank, re-scope, or explicitly replace the objective rather than silently treating creep as continuity.

action_logic: After major reviews, update the charter baseline or archive the expansion history so the system does not lose memory of the trajectory.

decision_rules: Accept a sub-objective only if it strengthens the protected outcome more than it dilutes attention, capacity, legitimacy, or measurement clarity.

decision_rules: Pair every addition with one of: a removal, a capacity increase, a lower-priority deferral, a sunset date, or a formal re-charter.

decision_rules: Treat additive changes as high risk when they change who is served, what counts as success, what must be optimized, or which tradeoffs are politically hidden.

decision_rules: Trigger a boundary review when the number, cost, or decision load of sub-objectives crosses a predefined threshold.

decision_rules: Use postponement when an addition is plausible but not needed to preserve the original objective now.

decision_rules: Use re-chartering when the expanded objective is legitimate and preferable but no longer the same objective.

decision_rules: Reject or prune additions that survive only because removal is politically or procedurally harder than addition.

decision_rules: Do not label a necessary ethical, safety, or legal correction as creep merely because it constrains the original plan.

target_invariants: The original objective remains visible as a reference state until deliberately replaced.

target_invariants: Sub-objective additions have owners, rationale, cost, and removal conditions.

target_invariants: The system preserves a practical path for subtraction, sunset, rollback, or postponement.

target_invariants: Opportunity cost is visible at the same decision point as the perceived benefit of expansion.

target_invariants: Cumulative drift is evaluated against the charter, not only against the immediately preceding state.

target_invariants: Re-chartering is legitimate when the objective truly changes; silent creep is not.

target_invariants: Stakeholder legitimacy and safety constraints are preserved while guarding against unowned expansion.

expected_outcomes: A clearer distinction between adapting the means and changing the objective.

expected_outcomes: Reduced accumulation of locally reasonable but collectively diluting sub-objectives.

expected_outcomes: Earlier pruning or deferral of additions that do not fit the objective envelope.

expected_outcomes: Improved priority clarity, capacity realism, and stakeholder expectation management.

expected_outcomes: Better memory of why the original objective was chosen and when it was formally changed.

expected_outcomes: Lower risk that a project, mission, policy, or product becomes overloaded while still pretending to pursue the original goal.

expected_outcomes: More legitimate re-chartering when new evidence or obligations genuinely warrant a changed objective.

Scope Creep Containment

short_definition: Controls additive expansion of a project, work, or responsibility perimeter when each local addition is judged against the current expanded baseline rather than the original charter.

distinctive_feature: Perimeter and deliverable expansion are foregrounded more than dilution of a strategic objective.

distinct_from_parent: The parent governs objective-boundary expansion; this variant governs work-scope or responsibility-perimeter expansion that may or may not change the underlying objective.

still_same_parent_because: Both use ratchet detection, charter memory, addition/removal symmetry, opportunity-cost exposure, and reset or re-charter gates.

use_when: The expanding boundary is primarily the work perimeter, deliverable set, or responsibility scope.

use_when: There is no owner of the perimeter trajectory.

use_when: The current state becomes the comparison point for further additions.

example: A focused release accumulates adjacent features, integrations, reporting, and exception support until the release boundary no longer fits the sprint or launch goal.

failure_modes: Treating every stakeholder request as in scope

failure_modes: Calling a changed objective a minor scope update

Mission Creep Containment

short_definition: Prevents a mandate or mission from expanding into adjacent social, political, commercial, or operational aims without explicit authorization.

distinctive_feature: Legitimacy and authority are central, not only execution capacity.

distinct_from_parent: This variant applies objective-boundary governance to mandate-bearing institutions and missions.

still_same_parent_because: It still requires charter memory, addition/removal logic, drift signals, accountability, and re-charter gates.

use_when: The actor has a mandate, mission, or charter with legitimacy constraints.

use_when: Additions are justified by adjacency or urgency but alter mandate boundaries.

use_when: Public trust, consent, or authority depends on bounded scope.

example: A narrow regulatory authority gradually expands into adjacent enforcement, advisory, data-collection, and market-shaping functions without new mandate review.

failure_modes: Using mission boundaries to block necessary accountability

failure_modes: Letting emergency exceptions permanently expand authority

Requirements Creep Containment

short_definition: Controls incremental requirement additions that transform a product, system, or contract beyond the original objective and capacity envelope.

distinctive_feature: The named additions are requirements, features, or specifications.

distinct_from_parent: It is a product, engineering, or contract variant of objective-boundary expansion.

still_same_parent_because: It shares the same ratchet, ledger, admission, removal, and re-charter structure.

use_when: Requirements keep accumulating after the original objective is agreed.

use_when: New requirements appear locally reasonable but collectively alter delivery risk.

use_when: Removing requirements is harder than adding them.

example: A requirements document accumulates stakeholder-specific exceptions and reporting features until the core product objective is no longer the central acceptance criterion.

failure_modes: Disguising a new product strategy as a requirement detail

failure_modes: Letting specification traceability replace objective traceability

Metric Objective Creep Containment

short_definition: Prevents added metrics, proxy goals, or dashboard criteria from becoming new objectives without explicit tradeoff governance.

distinctive_feature: Metrics are the medium through which the objective boundary expands.

distinct_from_parent: The parent covers any sub-objective accretion; this variant focuses on proxy and measurement accretion.

still_same_parent_because: It still governs addition, removal, dilution, and re-charter of the effective objective boundary.

use_when: A dashboard or scoring system keeps adding metrics.

use_when: Each metric is useful locally but changes behavior globally.

use_when: Teams begin optimizing the added criteria as goals.

example: A balanced scorecard keeps absorbing new metrics until no metric has enough priority to guide decisions.

failure_modes: Hidden value judgments in added criteria

Exception Accretion Control

short_definition: Prevents recurring exceptions from becoming de facto objectives, obligations, or rights without baseline review.

distinctive_feature: The addition pathway is repeated exception handling rather than explicit new objectives.

distinct_from_parent: The parent handles all sub-objective addition; this variant focuses on exception pathways that gradually redefine the rule.

still_same_parent_because: Exception accumulation follows the same one-way ratchet and requires the same re-charter or sunset logic.

use_when: Exceptions are granted repeatedly for good local reasons.

use_when: Exception withdrawal is harder than exception admission.

use_when: The exception class changes the effective objective or service boundary.

example: A support policy keeps absorbing one-off exceptions until exceptional treatment becomes an unplanned service objective.

failure_modes: Unequal treatment through informal additions

near_names:
Objective Creep Control
Goal Boundary Control
Mission Creep Prevention
Scope Creep Prevention
Requirements Change Control
Charter Drift Control