Selection Bias Correction

batch: Third Sweep Candidate Batch 02

aliases:
selected-sample remediation
participation-bias correction
survivorship-bias correction
sample-selection repair

related_primes: missingness observability uncertainty boundary classification fairness generalization feedback constraint

slug: causality

slug: missingness

slug: uncertainty

slug: boundary

slug: classification

slug: fairness

slug: generalization

slug: feedback

slug: constraint

essence_one_line: Diagnose how entry, participation, survival, visibility, or analytic inclusion made observed cases differ from a target population, then repair the evidence or bound the claim.

canonical_formula: target_population + observed_sample + selection_pathway + inclusion_model + correction_or_recollection + overlap_guard + sensitivity_bounds + validation -> target-relevant_evidence_with_honest_residual_uncertainty

structural_problem: Observed cases have already passed through nonrandom gates, so their distribution, associations, outcomes, and apparent priorities can differ systematically from the target even when measurement and analysis within the selected set are technically correct.

prose_essence:
Selection Bias Correction repairs evidence after nonrandom gates have already determined who or what becomes observable. The pattern begins from a crucial asymmetry: the analyst can see selected cases, but the decision concerns a broader target. Participation, survival, reporting, access, recording, publication, and analytic inclusion can each make the visible cases systematically different even when measurement inside the visible set is flawless.

The archetype therefore treats selection as an intervention pathway to reconstruct, not a generic warning label. It declares the target, maps each gate, characterizes observed and excluded cases, estimates or bounds inclusion, identifies which claims are distorted, chooses correction or recollection, protects against non-overlap and unstable weights, tests unobserved selection, validates against independent evidence, and preserves residual limits. Its product is not a magically unbiased number; it is target-relevant evidence whose empirical support, assumptions, and remaining blind spots are governable.

prose_when_to_use:
Use this archetype when the evidence already exists and the inclusion process can no longer be redesigned from scratch. Typical signals include voluntary response related to satisfaction, attrition related to outcomes, learning only from survivors, access barriers that shape who appears, reporting systems that miss vulnerable cases, or analyses conditioned on a gate influenced by multiple causes. The candidate is especially valuable when a credible frame, auxiliary data, follow-up study, administrative linkage, or sensitivity range can illuminate unseen cases.

Do not use it as a synonym for every biased analysis. If sampling is still prospective, design a representative sample. If values are missing within an otherwise appropriate sample, select a missingness-aware estimator. If combining groups produces reversal, correct aggregation. If a common cause links treatment and outcome, control confounding. Where no target, overlap, auxiliary evidence, or credible assumption can be established, the correct intervention may be to narrow the claim, collect new evidence, or decline point estimation.

prose_structural_problem:
Selection bias is generated by a pathway, not merely by unequal counts. A target unit must be eligible, reachable, invited, willing, persistent, visible, recorded, and retained in analysis. Variables that affect passage may also affect the outcome, and conditioning on passage can create associations that do not exist in the target. Survivors can make strategies look safer; respondents can make services look more satisfactory; published successes can make interventions look more reliable; and convenient records can redirect resources away from invisible need.

The observed sample alone often cannot reveal the distortion because the missing cases are absent by definition. Correction therefore depends on frame data, auxiliary variables, follow-up evidence, causal assumptions, and explicit bounds. A second structural danger is positivity failure: some target regions have no plausible observed analog. Mathematical weighting cannot create information there. A mature response distinguishes imbalance that can be corrected, uncertainty that can be bounded, and absence that requires recollection or narrower claims.

prose_intervention_logic:
Begin by fixing the target population, estimand, time window, and decision. Profile the observed sample without assuming it defines the target. Build an operational and causal map of eligibility, access, contact, participation, survival, reporting, recording, and analytic inclusion. For every gate, ask who controls it, what predicts passage, how it relates to outcomes, and what evidence exists about excluded cases. Diagnose which margins, associations, effects, or priorities are materially distorted.

Choose the least assumption-dependent viable response. Known design probabilities may support weighting; target margins may support raking; a frame may support propensity modeling; nonresponse may require follow-up; survivorship may require case reconstruction; poor overlap may require new collection or a narrower target. Publish weight stability and effective sample size, test unobserved selection, compare alternative methods, validate against evidence not used to fit the correction, and version the pathway as access and behavior change. The action sequence closes only when claim scope and residual uncertainty travel with the corrected result.

prose_key_components:
### Target Population and Claim

Defines the population, process, or case universe to which the corrected inference or decision is intended to apply.

Operationally, this component must be represented in a form that decision makers can inspect and update. State the estimand or operational claim, time window, units, and decision use before choosing a correction. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by anchoring every diagnosis and correction to a declared target rather than the visible cases. It is required because correction can optimize the observed sample while remaining irrelevant to the intended population Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Observed Sample Profile

Describes who or what is observed, with relevant attributes, outcomes, exposure histories, and observation channels.

Operationally, this component must be represented in a form that decision makers can inspect and update. Preserve provenance, timing, duplicate handling, and the difference between absence and non-observation. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by providing the empirical object whose selection history must be explained. It is required because the analysis treats a convenience set as though it were self-defining Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Selection Pathway Map

Traces eligibility, access, invitation, participation, survival, reporting, recording, and analytic inclusion gates.

Operationally, this component must be represented in a form that decision makers can inspect and update. Represent gates in causal and operational order, including who controls them and which variables influence passage. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by making the distortion-producing pathway inspectable. It is required because teams correct demographics while missing the actual gate that selected outcomes or survivors Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Inclusion Probability Model

Estimates or bounds each target unit's probability of appearing in the observed analytic set.

Operationally, this component must be represented in a form that decision makers can inspect and update. Use design probabilities when known and model-based probabilities only with explicit assumptions, diagnostics, and positivity checks. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by connecting selection mechanisms to correction weights or resampling priorities. It is required because correction strength becomes arbitrary and extreme weights silently dominate Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Excluded and Unseen Case Profile

Characterizes omitted, unreachable, nonresponding, attrited, censored, or otherwise invisible cases using available frame and auxiliary evidence.

Operationally, this component must be represented in a form that decision makers can inspect and update. Distinguish known exclusions from structurally unobservable cases and do not invent attributes unsupported by evidence. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by showing what the observed sample cannot reveal by itself. It is required because the correction assumes missing cases resemble observed cases without testing that assumption Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Selection Distortion Diagnosis

Determines which distributions, associations, effects, risks, or priorities are changed by the selection pathway.

Operationally, this component must be represented in a form that decision makers can inspect and update. Compare observed and target margins, simulate gate effects, and distinguish harmless imbalance from decision-relevant distortion. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by preventing blanket correction when selection is present but immaterial to the claim. It is required because adjustment adds variance and model dependence without reducing relevant bias Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Identification Assumption Register

Records the conditions under which the selected data can identify or bound the target quantity.

Operationally, this component must be represented in a form that decision makers can inspect and update. Name exchangeability, missing-at-random, transportability, positivity, exclusion, and measurement assumptions in plain language. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by separating empirical evidence from assumptions required by the correction. It is required because a mathematically complete estimate is presented as assumption-free Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Correction Strategy and Estimand

Selects the adjustment, augmentation, resampling, reconstruction, or bounded-claim strategy appropriate to the pathway and target.

Operationally, this component must be represented in a form that decision makers can inspect and update. Choose the cheapest defensible correction and explain whether it changes weights, cases, model, target, or claim scope. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by turning diagnosis into a governed remedial intervention. It is required because several incompatible corrections are mixed without a coherent estimand Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Weight Stability and Overlap Guard

Controls extreme weights, sparse strata, non-overlap, and extrapolation beyond supported target regions.

Operationally, this component must be represented in a form that decision makers can inspect and update. Publish effective sample size, weight distribution, trimming rules, and the target population lost when overlap fails. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by keeping mathematical correction from manufacturing unsupported information. It is required because a few cases carry most of the result or excluded regions are silently extrapolated Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Sensitivity and Partial-Identification Layer

Tests how conclusions change under plausible unobserved selection and reports bounds when point correction is not identified.

Operationally, this component must be represented in a form that decision makers can inspect and update. Vary gate effects, outcome differences, and dependence structures using domain-credible ranges rather than cosmetic perturbations. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by making irreducible selection uncertainty visible. It is required because one favored selection model is treated as the only possible data-generating process Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Post-Correction Validation

Evaluates whether corrected data reproduce held-out target margins, outcomes, or external benchmarks without introducing new distortions.

Operationally, this component must be represented in a form that decision makers can inspect and update. Use negative controls, calibration targets not used in fitting, subgroup checks, and alternative correction methods. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by testing the correction as an intervention rather than accepting it because it ran. It is required because weighted balance is mistaken for truth despite poor outcome or external validity Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

### Claim Limitation and Monitoring

Constrains communication to supported populations and establishes monitoring for changing selection pathways.

Operationally, this component must be represented in a form that decision makers can inspect and update. Version the selection map, document residual blind spots, and trigger redesign or recollection when gates or participation change. Its owner, evidence source, confidence, and update cadence should be explicit; otherwise the component becomes a decorative label rather than load-bearing structure.

The component connects to the archetype by keeping corrected conclusions aligned with evolving access and inclusion conditions. It is required because a one-time correction becomes permanent authority after the pathway drifts Reviewers should test it with ordinary, boundary, missing-data, and adversarial cases instead of assuming that a plausible description will behave correctly in use.

prose_common_mechanisms:
### Inverse-Probability Weighting

Weights observed cases by the inverse of an estimated or known inclusion probability to reconstruct a target distribution.

Use it when selection probabilities are estimable and overlap is adequate Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Inspect positivity, stabilize or trim extreme weights, and report effective sample size.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Weighting is one correction method; it does not supply the pathway map, assumptions, validation, and claim governance.

### Post-Stratification and Raking

Adjusts observed margins to known target totals across selected auxiliary dimensions.

Use it when reliable population margins exist and selection is explainable through those dimensions Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Do not claim correction for unmeasured outcome-related selection, and test sparse cells.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Margin calibration implements part of the strategy but is not the complete archetype.

### Selection-Propensity Model

Models the probability of observation or participation from frame, administrative, or contact variables.

Use it when a frame includes observed and unobserved units with common predictors Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Separate prediction accuracy from causal adequacy and avoid using post-selection colliders carelessly.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. A propensity model becomes useful only inside a declared target and identification argument.

### Sample-Selection Outcome Model

Jointly models selection and outcome processes when their unobserved determinants may be correlated.

Use it when outcomes are observed only after a nonrandom gate and credible identifying structure exists Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. State exclusion restrictions, distributional assumptions, and sensitivity to misspecification.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. The model cannot replace evidence for why selection occurs or justify weak instruments.

### Targeted Oversampling and Recontact

Collects additional cases from underrepresented, high-uncertainty, or high-leverage target strata.

Use it when correction from existing data would depend on extreme extrapolation Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Preserve consent, access, burden, and comparability; do not coerce hard-to-reach groups.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. New collection is one remediation route and may be preferable to elaborate modeling.

### Nonresponse Follow-Up Study

Samples nonrespondents or late responders to estimate how participation relates to relevant outcomes.

Use it when survey, service, or audit nonresponse may be informative Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Use multiple contact modes and record differential reachability without treating late response as perfect truth.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. The study supplies missing pathway evidence but still requires integration and limits.

### Survivorship Reconstruction

Reintroduces failed, exited, censored, dissolved, or otherwise missing historical cases into analysis.

Use it when visible current cases are selected by persistence or successful completion Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Define failure and exit consistently, recover denominators, and distinguish censoring from true absence.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Reconstruction addresses the survival gate but not every selection pathway.

### Causal Selection Diagram

Represents variables that affect inclusion and outcomes to reveal conditioning, collider, and transportability risks.

Use it when several plausible selection mechanisms compete or adjustment could open biasing paths Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Treat arrows as assumptions to challenge with experts and data, not as decorative certainty.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. The diagram diagnoses structure; correction also needs an estimand and operational method.

### Transportability Reweighting

Reweights source data toward a destination population using covariate distributions and effect-modification assumptions.

Use it when evidence comes from one setting but decisions concern another Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Check support, measurement equivalence, effect modifiers, and destination-specific constraints.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Transport is a selection correction only when the source-to-target pathway is explicit.

### Unobserved-Selection Sensitivity Analysis

Varies the prevalence and outcome influence of unmeasured selection drivers to bound conclusions.

Use it when point identification depends on untestable selection assumptions Implementation should expose inputs, thresholds or transformation rules, expected outputs, responsible actors, and evidence of performance. Use interpretable parameters and report threshold values that would reverse the decision.

This mechanism is not the archetype by itself. It instantiates the broader intervention only when connected to the complete component set, monitoring, exception handling, and revision logic. Sensitivity analysis does not repair data; it governs uncertainty and claim scope.

prose_failure_modes:
### Wrong Target

The correction represents an administratively convenient frame rather than the population implicated by the decision. Detect it through stakeholder and estimand review reveals excluded target groups Respond by redefine the target and repeat pathway, overlap, and validation work

### Incomplete Pathway Map

Analysts model response but omit earlier access, eligibility, survival, reporting, or analytic gates. Detect it through new data sources reveal selection before or after the modeled gate Respond by extend the causal and operational map and reassess identification

### Extreme-Weight Domination

A small number of low-probability cases determine the corrected result. Detect it through low effective sample size, high leverage, and unstable leave-one-out estimates Respond by recollect, trim with target disclosure, use augmentation, or narrow the claim

### Unmeasured Selection

Participation or survival depends on outcome-related variables absent from the correction data. Detect it through follow-up studies, negative controls, or sensitivity thresholds contradict the fitted model Respond by collect pathway evidence, bound results, or stop point identification

### Collider Adjustment Error

Conditioning or controlling within the selected set opens a spurious association. Detect it through causal selection diagrams and alternative conditioning sets produce reversals Respond by revise the graph, estimand, and correction strategy

### Correction Without Validation

Weighted balance is accepted although corrected outcomes fail external checks. Detect it through held-out margins, follow-up cases, or prospective outcomes remain miscalibrated Respond by reject or revise the model and communicate residual bias

prose_neighbor_distinctions:
### Representative Sampling Design

Prospectively constructs a sample whose inclusion process is designed before observation.

Boundary rule: Use Representative Sampling Design when the frame and selection plan can still be designed; use Selection Bias Correction when a nonrandom selection pathway has already produced the observed data and must be diagnosed, repaired, bounded, or supplemented.

Hybrid cases should assign each design obligation to its proper owner rather than using Selection Bias Correction as an umbrella for all adjacent work.

### Aggregation Bias Detection and Correction

Detects distortion caused by combining heterogeneous groups or levels.

Boundary rule: Use it when aggregation changes an estimate despite adequate inclusion; use Selection Bias Correction when entry, participation, survival, visibility, or analytic inclusion makes observed cases systematically unlike the target.

Hybrid cases should assign each design obligation to its proper owner rather than using Selection Bias Correction as an umbrella for all adjacent work.

### Missingness-Aware Estimator Selection

Chooses estimation procedures appropriate to missing-data patterns.

Boundary rule: Use it when incomplete variables within a defined sample are central; use Selection Bias Correction when entire cases or pathways into observation are nonrandom and target-population recovery is the design obligation.

Hybrid cases should assign each design obligation to its proper owner rather than using Selection Bias Correction as an umbrella for all adjacent work.

### Confounder Control

Controls common causes of exposure and outcome in causal estimation.

Boundary rule: Use it for treatment-outcome backdoor paths; use Selection Bias Correction for conditioning on observation, participation, survival, or other selection gates, including collider bias created by analytic inclusion.

Hybrid cases should assign each design obligation to its proper owner rather than using Selection Bias Correction as an umbrella for all adjacent work.

### Generalization Validation

Tests whether a finding or model transfers beyond its development setting.

Boundary rule: Use it to evaluate external performance broadly; use Selection Bias Correction when a specific selection pathway must be modeled and actively corrected before the claim can represent the target.

Hybrid cases should assign each design obligation to its proper owner rather than using Selection Bias Correction as an umbrella for all adjacent work.

### Bias-Specific Decision Audit

Audits a decision process for a named cognitive, procedural, or distributional bias.

Boundary rule: Use it when the decision rule or actor behavior is the intervention target; use Selection Bias Correction when inclusion into the evidence base is the distortion-producing mechanism.

Hybrid cases should assign each design obligation to its proper owner rather than using Selection Bias Correction as an umbrella for all adjacent work.

prose_cross_domain_examples:
### Clinical Evidence

Investigators combine trial and registry frames to model which patients entered a treatment study, reweight toward eligible patients, test overlap, and bound effects for poorly represented severity groups.

Why it fits: treatment evidence already comes from a selected participant pathway and the target is broader than enrolled patients The design is evaluated by weight stability, target-margin recovery, external outcome calibration, and sensitivity to unmeasured participation

### Workplace Safety

A firm reconstructs injuries among contractors and departed workers omitted from employee surveys, follows a sample of nonrespondents, and revises risk estimates and controls.

Why it fits: employment status and reporting access select which harms become visible The design is evaluated by recovered denominator, reporting-propensity differences, corrected risk ranking, and policy effect

### Product Analytics

A platform corrects satisfaction findings drawn only from active users by modeling churn and survey response, adding exit interviews, and limiting claims for unreachable accounts.

Why it fits: survival and voluntary response jointly select the observed experience The design is evaluated by held-out churn prediction, recontact evidence, effective sample size, and decision stability

### Education

A program evaluates outcomes after accounting for school opt-in, student attrition, and test participation, oversampling missing contexts and reporting bounds where overlap fails.

Why it fits: multiple gates select both institutions and measured students The design is evaluated by coverage of target schools, attrition reconstruction, sensitivity bounds, and subgroup calibration

### Public Consultation

A city maps access, invitation, language, attendance, and comment-submission gates, supplements the record with targeted outreach, and weights only where frame evidence supports it.

Why it fits: visible comments systematically overrepresent actors able and motivated to participate The design is evaluated by target-margin coverage, participation burden, policy-priority changes, and residual blind spots

### Organizational Learning

A company reconstructs project lessons including canceled and failed initiatives rather than learning only from surviving launches, then adjusts its portfolio guidance.

Why it fits: survivorship selects the cases available for retrospective inference The design is evaluated by failed-case recovery, changed success-factor estimates, and prospective prediction

prose_non_examples:
### Prospective Stratified Sample

Researchers still control the frame and draw strata before data collection. It fails the archetype boundary because the task is to design inclusion rather than remediate an already selected evidence set Route the case to Representative Sampling Design when that is the actual design object.

### Item-Level Imputation

A valid sample has sporadic missing answers and analysts impute them under a documented missingness model. It fails the archetype boundary because no case-level inclusion pathway or target-population distortion is central Route the case to Missingness-Aware Estimator Selection when that is the actual design object.

### Confounding Adjustment

Treatment and outcome share a common cause, but observation is not conditioned on a selection gate. It fails the archetype boundary because the causal problem is confounding rather than selection into the analytic set Route the case to Confounder Control when that is the actual design object.

### Aggregation Reversal

A relationship reverses after combining groups despite adequate representation within each group. It fails the archetype boundary because group aggregation, not selection into observation, produces the distortion Route the case to Aggregation Bias Detection and Correction when that is the actual design object.

prose_tradeoffs:
### Bias versus Variance

Strong reweighting can reduce systematic distortion while sharply reducing effective information. The design should not pretend this tension disappears. Use stabilized estimators, recollection, multiple corrections, and decision-focused loss analysis and make the selected position visible to affected actors.

### Breadth versus Support

A broad target improves relevance but may include regions absent from observed data. The design should not pretend this tension disappears. Use overlap diagnostics, target refinement, partial identification, and explicit unsupported strata and make the selected position visible to affected actors.

### Modeling versus Recollection

Model correction is fast but assumption-dependent; new data are costly but can restore support. The design should not pretend this tension disappears. Use value-of-information analysis targeted at the gates and strata driving uncertainty and make the selected position visible to affected actors.

### Visibility versus Privacy

Diagnosing selection often requires attributes and pathway records that create privacy and burden risks. The design should not pretend this tension disappears. Use data minimization, controlled linkage, consent, aggregation, and independent governance and make the selected position visible to affected actors.

prose_invariants_to_preserve:
### Declared Target

Every correction is tied to an explicit target population and estimand. Preserve it by versioning the target and checking that data, weights, and validation correspond to it A violation is indicated by the same corrected estimate is reused for incompatible populations

### Pathway Before Method

Correction follows an explicit selection-pathway diagnosis rather than an estimator chosen by habit. Preserve it by reviewing each gate, its causes, and its relation to outcomes before modeling A violation is indicated by teams apply demographic weights without explaining participation or survival

### Overlap Honesty

No correction claims information where target cases have no supported analog in observed data. Preserve it by publishing support diagnostics and narrowing or recollecting when positivity fails A violation is indicated by extreme weights or model-only predictions dominate unsupported strata

### Assumption Visibility

Empirical facts, identifying assumptions, and sensitivity choices remain distinguishable. Preserve it by maintaining an assumption register and alternative analyses A violation is indicated by a model output is communicated as direct observation

### Residual-Uncertainty Disclosure

Correction never erases uncertainty from unobserved selection or weak validation. Preserve it by reporting bounds, effective sample size, and unsupported regions with the result A violation is indicated by precision increases after correction without supporting information

### Independent Validation

At least some target evidence not used to fit the correction tests its consequences. Preserve it by holding out benchmarks, follow-up samples, or future outcomes A violation is indicated by success is defined only as balance on fitting variables

prose_target_outcomes:
The immediate outcome is evidence whose relationship to a declared target is explicit and testable. Decision makers can see which gates selected the observed cases, which correction assumptions are empirical or hypothetical, where overlap supports recovery, and where only bounds or recollection are defensible. Corrected estimates should improve target-margin calibration, external prediction, subgroup coverage, and decision stability without hiding variance inflation.

Secondary outcomes include recovery of failed and invisible cases, better prioritization of outreach and collection, reduced survivorship storytelling, fairer representation of hard-to-reach groups, and clearer separation between data limitations and substantive absence. The system should become capable of detecting pathway drift, such as changing response incentives or platform churn, and revising the correction rather than treating yesterday's weights as permanent truth.

prose_parameter_dimensions:
### Target Breadth

Sets how far the intended population extends beyond observed support. Low settings create an unnecessarily narrow but defensible claim; high settings create unsupported extrapolation into target regions with no overlap. Tune against frame coverage, decision scope, and overlap diagnostics and record the rationale.

### Correction Strength

Controls the influence of weights, modeled outcomes, augmentation, or reconstructed cases. Low settings create residual selection bias; high settings create variance inflation and domination by modeled assumptions. Tune against effective sample size, external calibration, and bias-variance analysis and record the rationale.

### Weight Trimming

Sets caps or stabilization rules for low inclusion probabilities. Low settings create unstable estimates driven by a few cases; high settings create reintroducing bias and silently narrowing the target. Tune against weight distribution, overlap, loss function, and sensitivity curves and record the rationale.

### Unobserved-Selection Sensitivity

Sets the range of plausible hidden gate effects considered. Low settings create false confidence from cosmetic perturbations; high settings create bounds so broad that the analysis cannot guide action. Tune against follow-up data, domain knowledge, negative controls, and comparable studies and record the rationale.

### Recollection Intensity

Determines investment in follow-up, oversampling, frame repair, or new collection. Low settings create continued dependence on extrapolation; high settings create cost, delay, privacy burden, and possible coercion. Tune against value of information and which additional cases most reduce decision uncertainty and record the rationale.

prose_variants_and_near_names:
### Nonresponse Bias Correction

Corrects distortion when participation or response depends on characteristics related to the target outcome.

Its load-bearing distinction is contact, response propensity, and follow-up evidence are load-bearing Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when nonresponse is ignorable after ordinary missing-data handling

### Survivorship Bias Correction

Restores failed, exited, censored, or dissolved cases omitted by observing only survivors.

Its load-bearing distinction is the selection gate occurs through persistence over time Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when the target claim intentionally concerns current survivors only

### Access and Visibility Bias Correction

Corrects evidence that overrepresents people or events easiest to reach, record, publish, or observe.

Its load-bearing distinction is institutional access and observation channels create the inclusion probability Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when the issue is merely a weak measurement of already-included cases

### Collider Selection-Bias Correction

Repairs associations distorted by conditioning on a common effect that controls inclusion.

Its load-bearing distinction is the analytic gate opens a noncausal path between otherwise separate causes Preserve it as a named variant when that difference changes design or failure analysis. Collapse it when the relationship is instead ordinary confounding or aggregation

raw_bundle_path: solution_archetypes/_Batches2/Third Sweep Candidate Batch 02/selection_bias_correction_solution_archetype_output_bundle.zip


type: archetype

source: generated

batch: Batch 01

canonical_family: Aggregation & Scale Artifacts

slug: decomposition

slug: aggregation

slug: diversification

slug: comparison

slug: proportionality

slug: invariance

slug: transformation

slug: scale

components: Decomposition Identity and Residual Check

mechanisms: Decomposition Residual Reconciliation Workflow

essence_one_line: When an aggregate mean changes, split the change into how much came from units gaining or losing weight and how much came from units changing internally.

compression_statement: Selection–Transmission Change Attribution is the intervention pattern for explaining a weighted-mean shift by explicitly separating differential representation of units from within-unit transformation, then routing action to composition, selection pressure, retention, learning, repair, or measurement according to the dominant term.

core_intuition: An average can rise because better units became more common, because every unit got better, or because both happened. Treating those causes as one aggregate change leads to the wrong response: change the selection environment when composition drives the shift; change learning, transmission, repair, or unit behavior when within-unit change drives it.

canonical_formula: Δ mean ≈ selection_term + transmission_term; selection_term tracks differential unit weights, transmission_term tracks within-unit value change, with residuals audited for unmatched units and measurement drift.

structural_problem: A population-level weighted mean changes, but the observer cannot tell whether the shift is caused by differential weighting of units, within-unit change, or a mixture of both, so explanations and interventions target the wrong level.

trigger_conditions: An average, rate, score, trait, performance metric, risk level, or capability changes across two states or periods.

trigger_conditions: Units have unequal weights, shares, survival probabilities, exposure, participation, frequency, or influence.

trigger_conditions: Units can also change internally between states through learning, degradation, adaptation, mutation, repair, or behavioral response.

trigger_conditions: Entry, exit, growth, shrinkage, or differential persistence changes which units dominate the aggregate.

trigger_conditions: Decision-makers are tempted to interpret the aggregate change as a uniform population improvement or decline.

trigger_conditions: The needed response differs depending on whether composition or within-unit transformation is driving the change.

symptoms: Aggregate improvement accompanied by stagnant or worsening continuing units.

symptoms: Aggregate decline despite many units improving internally because low-performing units gained weight.

symptoms: Debates over whether change reflects selection, learning, attrition, replacement, adaptation, or measurement artifacts.

symptoms: Misallocated interventions, such as training units when the problem is entry/exit mix, or changing selection criteria when the problem is transmission failure.

symptoms: Credit or blame assigned to units without accounting for their changing representation in the weighted mean.

symptoms: Residual change that cannot be reconciled because unit identity, weights, or measurement scales drifted between states.

root_tension: Aggregate change is one number, but it is produced by two structurally different channels: the population can change by reweighting its units, and units can change within themselves. The same observed shift can require opposite interventions depending on which channel dominates.

anti_signatures: All units have equal fixed weights and no entry, exit, or differential representation across the comparison window.

anti_signatures: The main issue is choosing an aggregation rule rather than explaining a change in an already-defined weighted mean.

anti_signatures: The data are a single cross-section with no transition, lineage, or before/after comparison.

anti_signatures: The problem is only a sampling-bias correction with no within-unit change term.

anti_signatures: The needed analysis is a causal treatment-effect estimate, not an accounting split of observed aggregate change.

anti_signatures: Unit correspondence cannot be defined even approximately, making selection and transmission terms uninterpretable.

structural_intervention: Define units, values, weights, and correspondence across states; calculate the observed weighted-mean change; split it into differential-weighting and within-unit-change terms; reconcile residuals; and use the dominant term pattern to select the appropriate intervention level.

action_logic: Declare the population boundary, unit identity rule, comparison states, and value being averaged.

action_logic: Record each unit’s value and weight in the starting state and comparison state, including entry, exit, splits, merges, and missingness.

action_logic: Normalize weights so the weighted mean is interpretable for each state and the comparison window.

action_logic: Build the correspondence map that distinguishes continuing units from new, removed, split, merged, or descendant units.

action_logic: Calculate the observed aggregate weighted-mean change.

action_logic: Estimate the selection term: change attributable to units gaining, losing, entering, exiting, surviving, or carrying different weights.

action_logic: Estimate the transmission term: change attributable to values changing within continuing or linked units.

action_logic: Recompose the terms to the observed change and investigate residuals caused by unmatched units, scale changes, measurement error, or normalization choices.

action_logic: Route action according to the split: adjust selection pressures, entry/exit rules, retention, weights, or representation when selection dominates; adjust learning, transmission, repair, adaptation, or within-unit process when transmission dominates.

action_logic: Rerun the decomposition under alternative windows, unit definitions, and weighting schemes before making high-stakes claims.

decision_rules: If the selection term dominates, ask which units are gaining weight and why before attributing change to universal improvement or decline.

decision_rules: If the transmission term dominates, investigate the within-unit process that altered values rather than focusing only on composition.

decision_rules: If selection and transmission have opposite signs, report the masking explicitly: one channel is hiding or offsetting the other.

decision_rules: If residuals are large, do not interpret the split until unit correspondence, entry/exit treatment, measurement scale, and weight normalization have been repaired.

decision_rules: If the intervention goal is equity or fairness, inspect which subpopulations gained weight and whether the aggregate hides harm to lower-weight groups.

decision_rules: If a repeated selection process is being managed, connect the selection term to available variance and future selection-response limits.

decision_rules: If units cannot be matched across states, downgrade the result to a composition analysis and flag the missing transmission evidence.

decision_rules: If the split is highly sensitive to window or unit definition, treat the conclusion as partition- and scale-dependent rather than stable.

target_invariants: The observed aggregate change must recompose from the reported terms plus declared residuals.

target_invariants: Unit values and unit weights must remain conceptually separate throughout the analysis.

target_invariants: Selection, entry, exit, and reweighting must not be mislabeled as within-unit learning or improvement.

target_invariants: Within-unit transformation must not be hidden inside composition change merely because reporting is aggregate.

target_invariants: The unit correspondence rule must be explicit enough for another reviewer to reproduce the split.

target_invariants: Weights must be normalized and interpreted consistently across the comparison states.

target_invariants: The split is an attribution/accounting structure; causal interpretation requires additional design evidence.

expected_outcomes: Aggregate change is decomposed into interpretable selection and transmission contributions.

expected_outcomes: Composition-driven and within-unit-driven changes no longer receive the same explanation or intervention response.

expected_outcomes: Masking cases become visible, such as aggregate improvement hiding decline among continuing units.

expected_outcomes: Residuals and data-quality defects are separated from substantive change attribution.

expected_outcomes: Strategic action is better targeted to selection pressure, portfolio mix, retention, learning, repair, or measurement.

expected_outcomes: Future monitoring can track whether interventions alter the intended term rather than merely moving the aggregate.

Price Equation Attribution

short_definition: A formal variant using the Price-equation style identity to split aggregate change into covariance/selection and expectation/transmission terms.

distinctive_feature: The split is an exact accounting identity rather than a regression, narrative causal story, or generic decomposition.

distinct_from_parent: The parent includes any practical selection/transmission attribution; this variant emphasizes the canonical Price-equation mathematical form.

still_same_parent_because: It uses the same components: unit scope, weights, values, correspondence, selection term, transmission term, and recomposition check.

use_when: The population can be represented as units with weights and values across states.

use_when: Differential persistence or representation and within-unit change are both plausible causes of aggregate change.

example: A trait mean rises partly because high-trait lineages leave more descendants and partly because descendants alter the trait relative to parents.

failure_modes: The identity is overread as causal proof without validating units, weights, and correspondence.

Composition-vs-Capability Change Split

short_definition: A management and analytics variant that asks whether an aggregate metric changed because the mix of units changed or because each unit improved or degraded.

distinctive_feature: It translates the formal split into operational language for portfolios, cohorts, and organizational units.

distinct_from_parent: The parent is cross-domain and includes evolutionary lineages, epidemiology, economics, and learning systems.

still_same_parent_because: The same weighted-mean change identity governs the analysis.

use_when: A portfolio, team, customer base, fleet, or cohort changes average performance across periods.

use_when: The decision response differs depending on whether the driver is mix shift or within-unit transformation.

example: Average engagement rises because heavy users now form a larger share, not because each user segment became more engaged.

failure_modes: The organization praises or blames unit-level change when the driver was mainly composition shift.

Selection-Response Bounds Linkage

short_definition: A variant that connects the observed selection term to available variance and selection intensity so expected response bounds can be checked.

distinctive_feature: It links attribution of past change to limits on future selectable response.

distinct_from_parent: The parent attributes current aggregate change; this variant adds response-bound interpretation over repeated selection rounds.

still_same_parent_because: It depends on first separating the selection term from the transmission term.

use_when: The selection contribution is large enough that available variance may constrain future response.

use_when: A system is being managed for adaptive response over repeated rounds rather than a single before/after attribution.

domain: breeding

example: A program checks whether observed improvement comes from selecting high-value variants and whether variance remains for future gains.

failure_modes: The variance-bound interpretation is applied where weights are policy-assigned rather than selection-like.

near_names:
Selection Vs Transmission Decomposition
Price Equation Decomposition
Between–Within Change Decomposition
Composition–Transformation Split
Weighted Mean Change Decomposition


Selective Legacy Integration

batch: Batch 13

slug: dependency

slug: institution

slug: interoperability

slug: versioning

slug: enculturation

slug: continuity

components:
Rupture Boundary Definition
Legacy Element Inventory
Continuity Invariant Set
Value-Risk Classification Rule
Successor Context Mapping
Translation or Adapter Path
Institutional Memory Repository
Continuity Stewardship Role
Stakeholder Memory Channel
Sunset, Archive, and Repudiation Rule
Dependency and Provenance Map
Continuity Validation Loop
Parallel Practice Window
Narrative Continuity Frame
Mentor or Apprenticeship Bridge

mechanisms: Legacy Element Keep / Translate / Sunset Matrix

essence_one_line: Carry forward what gives a predecessor system knowledge, trust, and identity while redesigning it for the successor context.

compression_statement: Selective Legacy Integration treats organizational rupture as an inheritance-design problem: map the valuable dependencies and memory of the predecessor, distinguish generative legacy from obsolete drag, translate preserved elements into the successor structure, and leave a traceable archive for what is retired.

core_intuition: A clean break often destroys tacit knowledge, legitimacy, user trust, and the practical reasons old arrangements worked; uncritical preservation keeps obsolete constraints alive. The useful middle path is selective inheritance: carry forward the identity-bearing and knowledge-bearing parts in a form the new system can actually use.

canonical_formula: organizational_rupture + valuable_legacy_dependency -> inventory + classify + translate + embed + archive/sunset

structural_problem: A successor organization, system, role structure, or practice regime must replace or transform a predecessor, but the predecessor contains tacit knowledge, trusted relationships, provenance, symbolic continuity, or identity-defining practices that will be lost if the transition is treated as simple replacement.

trigger_conditions: A merger, acquisition, reorganization, leadership transition, regime change, or platform migration creates a before/after boundary.

trigger_conditions: People depend on inherited routines, records, relationships, or meanings that are not fully documented in the new design.

trigger_conditions: The successor needs legitimacy with stakeholders who still recognize the predecessor as meaningful or trustworthy.

trigger_conditions: A legacy system, policy, archive, community, or practice contains business rules or cultural memory that cannot be discarded safely.

trigger_conditions: A transition plan emphasizes replacement, sunset, or modernization but has no explicit account of what institutional memory must survive.

symptoms: Repeated questions about why old practices existed after the people who understood them have left.

symptoms: New processes technically work but lose user trust, continuity of care, historical accountability, or local know-how.

symptoms: Teams recreate old exceptions, workarounds, or rituals informally because the successor structure lacks their functional equivalents.

symptoms: Stakeholders experience the change as erasure, not renewal.

symptoms: Data, documents, or artifacts are migrated while meanings, norms, decision rationales, and relationships are not.

root_tension: The system must cross a rupture boundary without either freezing the predecessor in place or erasing the dependencies, knowledge, and identity that make continuity meaningful.

anti_signatures: The old arrangement has no continuing dependency, knowledge value, trust value, or identity value.

anti_signatures: The goal is a deliberately clean break where continuity would reproduce harm or illegitimate authority.

anti_signatures: The problem is only technical compatibility between versions, with no organizational memory or identity stakes.

anti_signatures: The issue is only ongoing self-maintenance in a stable system rather than transition across rupture.

anti_signatures: Preservation is desired only as archival storage with no need to embed selected elements in a successor system.

structural_intervention: Create an explicit inheritance pathway: identify legacy elements, classify their value and risk, define continuity invariants, translate selected elements into the successor context, archive or sunset the rest, and monitor whether the successor preserves usable memory and recognizable identity without retaining obsolete constraints.

action_logic: Draw the rupture boundary between predecessor and successor so the relevant discontinuity is explicit.

action_logic: Inventory knowledge-bearing, identity-bearing, trust-bearing, and dependency-bearing legacy elements.

action_logic: Classify each legacy element as keep, translate, adapt, archive, sunset, or explicitly repudiate.

action_logic: Define continuity invariants: the capabilities, meanings, relationships, records, or norms that must remain recognizable.

action_logic: Map preserved elements into successor roles, artifacts, interfaces, rules, training, rituals, or repositories.

action_logic: Assign stewardship for the inherited elements so they do not become orphaned or informally re-created.

action_logic: Create a retrieval and provenance path for archived elements and an exit rule for obsolete elements.

action_logic: Validate with affected stakeholders whether the successor preserves what matters without reproducing harmful lock-in.

decision_rules: Preserve an element when it carries capability, trust, accountability, identity, legal/provenance value, or hard-won tacit knowledge.

decision_rules: Translate rather than copy when the element is valuable but encoded in obsolete roles, media, language, software, or informal norms.

decision_rules: Archive rather than embed when the element is historically important but should not constrain present operation.

decision_rules: Sunset or repudiate when the element reproduces harm, blocks necessary transformation, or persists only through inertia.

decision_rules: Require a named successor owner for every preserved element that affects ongoing decisions or user experience.

decision_rules: Test preservation against continuity invariants, not against surface resemblance to the old system.

target_invariants: Usable institutional memory survives the transition.

target_invariants: Identity-bearing principles remain legible after translation into new structures.

target_invariants: Critical dependencies from the predecessor are either preserved, replaced, or deliberately retired with a rationale.

target_invariants: Stakeholders can understand what was carried forward, what changed, and why.

target_invariants: Obsolete or harmful legacy constraints do not persist invisibly under the banner of continuity.

target_invariants: Archived history remains retrievable enough for accountability, learning, and future reinterpretation.

expected_outcomes: Reduced loss of tacit knowledge and historical rationale during transition.

expected_outcomes: Greater legitimacy and trust in the successor system among legacy stakeholders.

expected_outcomes: Cleaner separation between valuable inheritance and legacy drag.

expected_outcomes: Fewer shadow processes created to compensate for erased practices.

expected_outcomes: Better continuity of service, care, identity, or mission across organizational discontinuity.

expected_outcomes: A traceable record of why particular legacy elements were kept, translated, archived, or retired.

Knowledge Continuity Integration

short_definition: Preserves tacit and explicit knowledge across turnover, migration, merger, or role redesign.

distinctive_feature: The core inheritance object is knowledge rather than symbolic identity or technical compatibility.

distinct_from_parent: It narrows the parent archetype to know-how capture, translation, and transfer.

still_same_parent_because: The pattern still integrates predecessor legacy into a successor structure across discontinuity.

use_when: Critical know-how is concentrated in people, local routines, exceptions, cases, or informal workarounds.

use_when: A successor team or system will operate without the original knowledge holders or context.

example_domains:
workforce transition
software modernization
public administration

domain: operations

example: A departing expert shadows the successor while building a decision-rationale library for exceptional cases.

failure_modes: Capturing documents without tacit context.

failure_modes: Treating old workaround knowledge as automatically valid in the new context.

Institutional Continuity Integration

short_definition: Preserves the rules, roles, expectations, and legitimacy of an institution across reform, merger, or succession.

distinctive_feature: The core inheritance object is institutional legitimacy and role continuity.

distinct_from_parent: It narrows the parent to institutional governance and formal/informal structure translation.

still_same_parent_because: It remains a selective legacy integration pattern across a discontinuity.

use_when: An institution changes formal structure but must remain recognizable to members or stakeholders.

use_when: Informal norms and relationships carry practical authority not captured by formal charts.

example_domains:
governance reform
nonprofit merger
university restructuring

failure_modes: Preserving legitimacy language while erasing the practices that earned it.

failure_modes: Letting informal power persist without accountability.

Heritage Preservation Integration

short_definition: Preserves cultural, symbolic, narrative, or community heritage by translating it into successor interpretation and practice.

distinctive_feature: The inheritance object is symbolic, cultural, or narrative continuity.

distinct_from_parent: It narrows the parent to heritage and collective-memory integration.

still_same_parent_because: It still selects, translates, embeds, and archives legacy elements across rupture.

use_when: Heritage artifacts or narratives shape identity and legitimacy in the present.

use_when: A successor institution must preserve memory while revising outdated or harmful representations.

example_domains:
museums
community archives
brand or mission renewal

failure_modes: Sanitizing difficult history in the name of continuity.

failure_modes: Freezing heritage as nostalgia instead of translating living principles.

Legacy System Knowledge Integration

short_definition: Carries old-system business rules, user expectations, exceptions, and operational rationale into a modernized technical system.

distinctive_feature: The domain is technical migration, but the important object is knowledge and meaning, not just code or data.

distinct_from_parent: It narrows the parent to platform, software, or data-system modernization contexts.

still_same_parent_because: It is a domain-specific case of selective legacy integration rather than a purely technical archetype.

use_when: A technical migration risks losing the business logic and work practices encoded in a legacy system.

use_when: A compatibility layer alone would preserve interfaces but not the reasons old behavior mattered.

example_domains:
enterprise software modernization
public-benefits platform migration

failure_modes: Preserving obsolete behavior because it exists in the old system.

failure_modes: Migrating data while losing exception logic and human judgment.

near_names:
Legacy Integration
Heritage Preservation
Institutional Continuity
Knowledge Continuity
Institutional Legacy Management


Selective Pathway Suppression

batch: Batch 15

canonical_family: Thresholds, Barriers & Phase Change

aliases:
Targeted Transformation Inhibition
Controlled Pathway Suppression
Active Pathway Braking
Mechanism-Selective Inhibition
Selective Inhibitory Control
Targeted Process Suppression

slug: inhibition

slug: controllability

slug: catalysis

slug: metastability

slug: threshold

slug: observability

slug: monitoring

slug: damping

slug: flow

slug: bycatch

slug: competition

slug: tolerance

slug: homeostasis

slug: equilibrium

slug: irreversibility

slug: externality

slug: fairness

slug: consent

slug: legitimacy

slug: accountability

slug: resilience

slug: robustness

slug: nonlinearity

slug: coupling

slug: network

slug: diffusion

slug: attention

slug: prioritization

slug: deterrence

slug: amplification

slug: adaptation

components:
Target Transformation Specification
Protected Function Set
Active Mechanism Map
Inhibitory Control Point
Inhibitory Agent Definition
Target–Non-Target Selectivity Map
Inhibition Objective Band
Baseline and Counterfactual Measure
Dose–Occupancy–Response Model
Onset, Duration, and Clearance Model
Reversibility and Release Rule
Off-Target Effect Monitor
Bypass and Compensatory-Path Monitor
Resistance and Escape Monitor
Rebound and Withdrawal-Risk Model
Monitoring and Feedback Loop
Safety Margin
Stop and Rollback Rule
Accountable Inhibition Authority
Exception and Appeal Path
Combination-Inhibitor Interaction Map
Spatial–Temporal Targeting Window

mechanisms:
Competitive Occupancy Inhibition
Noncompetitive or Allosteric Inhibition
Parallel Feedforward Brake
Lateral Suppression Network
Feedback-Gain Reduction
Rate Limit or Throttle
Circuit-Breaker Pause
Feature-Flag Disablement
Permission or Access Revocation
Counter-Signal Injection
Goal-Shielding Protocol
Inhibitor Titration and Taper
Washout and Rechallenge
Decoy Binding or Sink
Time-Bounded Veto or Hold

essence_one_line: Slow, pause, or stop a specific active transformation by applying a selective counter-agent at its enabling mechanism while preserving protected functions and a monitored release path.

compression_statement: When an otherwise-active transformation is harmful, premature, excessive, or temporarily unsafe, define the target and protected non-target functions; map the mechanism that carries it; choose a control point and counteracting agent; establish the minimum effective intensity, selectivity window, onset, duration, and clearance; monitor off-target effects, bypass, resistance, and rebound; and taper, release, roll back, or retire the control under legitimate authority.

core_intuition: A system need not be shut down merely because one active pathway is producing harm. If the pathway has an identifiable enabling mechanism, a well-chosen counteracting relation can reduce that pathway more precisely than blanket friction or removal. The leverage is safe only when target suppression is distinguished from collateral suppression and when the brake can be observed, challenged, and released.

canonical_formula: Useful inhibition ≈ target-path reduction × selectivity × controllability × reversibility; subject to protected-function floors, bypass detection, resistance, duration, and legitimate authority.

structural_problem: A transformation is already active and is producing an undesirable, premature, excessive, or unsafe result. Full shutdown, removal, or blanket restriction would sacrifice important adjacent functions, yet passive warnings or generic friction do not act on the mechanism that carries the transformation. The system needs a selective brake that can reduce target activity while preserving legitimate operation and remaining reversible and observable.

trigger_conditions: The target transformation and its enabling mechanism can be specified with enough causal confidence to intervene.

trigger_conditions: Some target activity should be reduced while protected non-target functions should continue.

trigger_conditions: A counteracting agent, rule, signal, permission change, or state transition can reach a meaningful control point.

trigger_conditions: Target and protected-function effects can be monitored over the intervention's onset, duration, and release.

trigger_conditions: The intervention can be bounded by scope, intensity, time, authority, and stop conditions.

trigger_conditions: Rate limiting, boundary filtering, separation, containment, or total shutdown would be materially less precise or less reversible.

symptoms: An unwanted transformation continues even though the system recognizes its harm.

symptoms: Broad shutdown controls suppress valuable functions along with the target.

symptoms: Activity falls at the visible path but reappears through bypasses, substitutions, or shadow processes.

symptoms: Increasing control intensity produces diminishing target benefit and rising collateral cost.

symptoms: The brake remains active after its justification expires because no release or clearance model exists.

symptoms: Removing the brake causes rebound, backlog release, overshoot, or destabilization.

symptoms: Affected actors cannot tell who authorized the inhibition, how it is measured, or how to contest an error.

root_tension: The intervention must be strong enough to reduce a consequential active pathway yet selective enough to preserve adjacent function; persistent enough to work yet reversible enough to release; adaptive enough to counter escape yet restrained enough not to select for resistance or normalize indefinite suppression.

anti_signatures: The desired task is to accelerate a permitted transformation through a reusable facilitator; use Catalytic Pathway Enablement.

anti_signatures: The only issue is aggregate consumption or admission rate and a quota is sufficient; use Rate Limiting.

anti_signatures: The main intervention point is a boundary crossing; use Boundary Permeability Control.

anti_signatures: The harm is propagation across a network or medium; use Diffusion Containment.

anti_signatures: The correct action should be physically impossible in all ordinary cases; use Physical-Constraint Design for Impossibility.

anti_signatures: The target must be removed from circulation rather than left present but inactive; use Sequestration Containment.

anti_signatures: No valid target mechanism, non-target set, monitoring path, or legitimate authority can be established.

anti_signatures: The proposal suppresses people, identities, dissent, lawful competition, or protected participation rather than a specific harmful transformation.

structural_intervention: Define a specific active transformation and protected functions; map the mechanism and control point; introduce a selective counteracting agent or rule; calibrate an objective band, intensity, timing, duration, and clearance; monitor target reduction, non-target effects, bypass, resistance, and rebound; and govern activation, exception, taper, release, rollback, and review under explicit authority.

action_logic: Specify the target transformation, harmful outcome, normal operating context, and evidence that reducing it is justified.

action_logic: Name protected functions, actors, rights, safety services, and minimum activity floors that inhibition must not erase.

action_logic: Map the active causal or operational mechanism, including alternate paths and shared dependencies.

action_logic: Select a control point and inhibitory relation with measurable causal leverage and bounded blast radius.

action_logic: Define the residual target-activity band, selectivity window, safety margin, onset, duration, clearance, and release conditions.

action_logic: Establish baseline and counterfactual measures for target and protected functions.

action_logic: Test the intervention in the smallest safe and reversible scope, beginning at the minimum effective intensity.

action_logic: Monitor off-target effects, bypass, compensatory activation, resistance, tolerance, displacement, and hidden accumulation.

action_logic: Titrate, combine, narrow, reposition, or suspend controls only through predeclared decision rules and evidence.

action_logic: Taper or remove inhibition, verify recovery, manage rebound or backlog release, and retain an audit and learning record.

decision_rules: Prefer the narrowest control point and lowest intensity that places target activity inside the objective band.

decision_rules: Do not treat complete shutdown as the default; zero target activity must be justified separately from safe reduction.

decision_rules: If target reduction cannot be separated from unacceptable protected-function loss, stop and redesign rather than increasing intensity.

decision_rules: A plateau in effect should trigger saturation, mechanism, adherence, bypass, or resistance diagnosis—not automatic escalation.

decision_rules: If harm migrates to an alternate pathway, measure the total outcome and either broaden the mechanism model or switch archetypes.

decision_rules: Use feedforward inhibition only when the initiating signal reliably predicts the need for braking and timing can be calibrated.

decision_rules: Use lateral inhibition only when local contrast is the objective and a minimum activity floor prevents winner-take-all collapse.

decision_rules: Every consequential human or institutional control requires scoped authority, time limits, records, review, and an accessible exception or appeal path.

decision_rules: Release or taper when the trigger resolves, when the objective band is maintained without the brake, or when residual harm exceeds benefit.

decision_rules: Treat failed reversal, delayed clearance, severe rebound, or unauthorized scope growth as stop-rule events.

target_invariants: Protected functions remain above their specified viability or rights floor.

target_invariants: The target, control point, and authorizing purpose remain explicit and do not silently expand.

target_invariants: Target and non-target effects remain observable enough to support timely correction.

target_invariants: The intervention remains reversible or has an explicit irreversible-decision review boundary.

target_invariants: Exceptions, urgent overrides, and appeals remain available where consequential interests are affected.

target_invariants: Suppressed activity is not merely displaced into an unmonitored pathway or population.

target_invariants: Inhibition does not become permanent by inertia; renewal requires evidence and authority.

expected_outcomes: Target activity enters and remains within a justified residual-activity band.

expected_outcomes: Protected functions continue with less collateral loss than blanket shutdown or restriction.

expected_outcomes: The causal effect of the inhibitor is distinguishable from natural variation and unrelated interventions.

expected_outcomes: Off-target effects, bypass, resistance, and rebound are detected earlier and acted on explicitly.

expected_outcomes: Activation, titration, renewal, release, and rollback decisions become auditable and contestable.

expected_outcomes: The system can resume normal operation without uncontrolled residual inhibition or backlog release.

Feedforward Inhibition

variant_type: subtype

short_definition: Recruit an inhibitory path from the same input that activates the target response so magnitude, latency, or duration is shaped before outcome feedback arrives.

distinctive_feature: The brake is co-triggered in parallel with activation rather than waiting for a deviation signal.

distinct_from_parent: Adds a two-path timing architecture in which response is the difference between activation and inhibition, making delay and gain matching central.

still_same_parent_because: It deliberately counteracts an active transformation through a selective, measurable inhibitory path with protected-function and release requirements.

use_when: The initiating signal is observable before harmful output develops.

use_when: A transient response is useful but overshoot or persistence is not.

use_when: Activation and braking paths can be calibrated and fail independently.

example_domains:
neuroscience
safety interlocks
high-risk workflow governance

domain: workflow

example: A high-risk request simultaneously starts processing and an independent verification hold whose calibrated release shapes the response window.

failure_modes: The brake arrives too late to prevent overshoot.

failure_modes: The brake dominates and erases useful response.

failure_modes: A shared trigger failure disables both paths.

Lateral Inhibition

short_definition: Let active units suppress neighboring alternatives so local contrast, differentiation, or priority is sharpened without globally silencing the field.

distinctive_feature: Suppression is relational and local: activity in one unit inhibits neighboring units rather than acting through a single central brake.

distinct_from_parent: Adds topology, suppression radius, competition geometry, edge effects, and winner-concentration risks.

still_same_parent_because: The intervention still reduces an active pathway through a designed counteracting relation with selectivity and monitoring.

use_when: The problem depends on relative contrast among neighboring alternatives.

use_when: A meaningful neighborhood relation can be defined.

use_when: The system can preserve minority signals and a baseline activity floor.

example_domains:
sensory processing
pattern recognition
distributed prioritization

domain: information processing

example: A local competition layer suppresses neighboring weak activations to sharpen a feature boundary while retaining a nonzero floor.

failure_modes: Winner-take-all collapse removes legitimate alternatives.

failure_modes: Local dominance compounds into systemic exclusion.

Goal Shielding

short_definition: While a legitimate focal goal is active, temporarily suppress access to competing goals or cues and release that suppression when the goal ends or a timeout is reached.

distinctive_feature: The target is access to competing goals, and the release condition is tied to focal-goal completion rather than indefinite priority.

distinct_from_parent: Adds attention, motivational conflict, cue salience, deferred-goal recovery, and tunnel-vision risks.

still_same_parent_because: A bounded inhibitory rule counteracts competing activation while preserving protected exceptions, monitoring, and release.

source_primes: goal_shielding inhibition attention

use_when: Competing goals repeatedly interrupt a bounded focal task.

use_when: Urgent exceptions can bypass suppression.

use_when: Deferred goals will be surfaced and reviewed after release.

example_domains:
knowledge work
learning
attention management

domain: knowledge work

example: A focus interval suppresses routine notifications, preserves urgent escalation, and ends with a required deferred-item review.

failure_modes: Tunnel vision hides changing priorities.

failure_modes: Deferred goals return as an unmanageable rebound backlog.

failure_modes: Suppression becomes a permanent avoidance strategy.


Selectivity-Window Calibration

batch: Batch 12

canonical_family: Selectivity & Bounded Windows

slug: threshold

slug: boundedness

slug: classification

slug: calibration

slug: bycatch

slug: screening

slug: gatekeeping

slug: permeability

slug: monitoring

slug: feedback

slug: observability

slug: validation

slug: tolerance

slug: catalysis

slug: underspecification

slug: mass

components: Upper Selectivity Bound and Reversal Guard

mechanisms:
Selectivity Curve Sweep
Operating Band Specification
Challenge-Panel Cross-Reactivity Test
ROC or Precision–Recall Surface Review
Bycatch Audit
Window Drift Control Chart
Selective Admission Band Protocol

essence_one_line: Tune the operating band of a selector so it keeps distinguishing the intended target from near-targets and non-targets instead of becoming too weak, too broad, or reversed.

compression_statement: Selectivity-window calibration treats discrimination as a range-limited capability. A process may be selective only between a lower bound, where it first becomes strong enough to pick out the target, and an upper bound, where more intensity, strictness, temperature, dose, confidence, or pressure begins to capture non-targets, trigger side effects, saturate the substrate, or reverse the intended preference. The archetype maps that band, operates inside it, monitors bycatch and drift, and defines fallback behavior when selectivity cannot be maintained.

core_intuition: A knob that improves performance at first can stop improving it later. Turning the knob up may help a filter find targets, a reaction produce the desired product, or a policy catch violations. But beyond a point, the same knob can make the process indiscriminate: side products form, false positives surge, legitimate cases are swept in, or the target itself is damaged. Good design therefore asks not only "does more action work?" but "where does action remain selective?"

canonical_formula: valid_selectivity_window = {p | target_response(p) ≥ minimum_effective_response and non_target_burden(p) ≤ maximum_acceptable_burden and reversal_risk(p) ≤ guard_limit}; operate within guard_banded(valid_selectivity_window) and recalibrate when drift alters the response surface

structural_problem: A system relies on a selector, filter, reaction, rule, classifier, dose, or intervention whose ability to distinguish the intended target from non-targets depends on an operating parameter. The parameter is often treated as a one-way intensity knob, but discrimination only holds inside a bounded range. Outside that range the process may miss the target, capture the wrong things, produce side effects, amplify bycatch, or reverse its preference while still appearing successful under a narrow target metric.

trigger_conditions: The same process can act on desired targets and unwanted near-targets, side channels, vulnerable non-targets, or background variation.

trigger_conditions: There is a control parameter that can be tuned, chosen, swept, or monitored: temperature, dose, pressure, pH, strictness, confidence threshold, review depth, enforcement intensity, time-in-state, exposure, or resource concentration.

trigger_conditions: The target response and non-target burden are nonlinear, non-monotonic, saturating, or regime-dependent.

trigger_conditions: Operators are tempted to increase intensity because a target-only metric improves, while collateral capture or reversal is less visible.

trigger_conditions: There is evidence of false positives, false negatives, side products, bycatch, over-enforcement, cross-reactivity, or subgroup-specific collapse.

trigger_conditions: A child or neighboring window such as a therapeutic window, readiness window, or discourse window is too narrow to describe the whole control-parameter selectivity problem.

trigger_conditions: Window drift is likely because target definitions, substrates, instruments, populations, adversaries, or environmental conditions change.

trigger_conditions: The system needs a hold, abstain, fallback, or human-review mode when selectivity cannot be guaranteed.

symptoms: A process improves a target metric while non-target burden rises.

symptoms: Operators argue over whether the process is too strict or too permissive without a two-sided response map.

symptoms: A decision threshold that works in one context creates false positives or false negatives in another.

symptoms: Higher dose, temperature, enforcement, or model confidence starts causing side effects, side products, or collateral harm.

symptoms: Failures cluster near a boundary or in a subgroup whose valid window differs from the average window.

symptoms: A selector that once worked begins failing after population, material, demand, or adversary drift.

symptoms: Ambiguous cases are forced through a binary gate rather than routed to review or abstention.

symptoms: Success dashboards count target capture but do not count non-target capture.

root_tension: Selective action requires enough intensity to separate signal from background, but excessive or mislocated intensity destroys the distinction by sweeping in non-targets, saturating the substrate, creating side effects, or changing the mechanism being exploited.

anti_signatures: The only issue is staying far from a failure limit, with no target/non-target discrimination to preserve.

anti_signatures: The only issue is a single decision cutoff with known false-positive and false-negative costs and no operating-band, reversal, drift, or fallback problem.

anti_signatures: The system needs broad admission, boundary permeability, or prioritization rules rather than parameter-dependent selectivity.

anti_signatures: The relevant window is only a therapeutic benefit-harm range already covered by therapeutic-window management.

anti_signatures: The problem is one of temporal receptivity, social sayability, or local input-field coverage rather than discriminative performance across a control parameter.

anti_signatures: The target and non-target classes cannot be defined or sampled well enough to measure selectivity.

anti_signatures: A nonselective brute-force intervention is explicitly acceptable and non-target burden is irrelevant, safe, or fully reversible.

anti_signatures: The parameter cannot be controlled, bounded, observed, or used to trigger a fallback path.

structural_intervention: Define target and non-target reference cases, map selectivity across the relevant control-parameter range, identify lower and upper bounds plus reversal zones, specify a guarded operating band, monitor non-target burden and drift, and switch to recalibration, review, or fallback when the process leaves the valid selectivity window.

action_logic: Name the selector and the discriminative job it is expected to perform.

action_logic: Define target classes, non-target classes, near-neighbor confusions, and unacceptable collateral outcomes.

action_logic: Identify the control parameter or parameter surface that changes selectivity.

action_logic: Sweep or backtest the parameter range with separate measurement of target success, non-target burden, and uncertainty.

action_logic: Locate the lower bound where selectivity becomes usable and the upper bound where extra intensity becomes indiscriminate, harmful, saturating, or reversed.

action_logic: Choose a nominal operating band with guard margins, review zones, and exception authority.

action_logic: Instrument bycatch, cross-reactivity, missed targets, subgroup-specific collapse, and environmental drift.

action_logic: Create a fallback or abstention path for conditions outside the band or cases that cannot be classified selectively.

action_logic: Version and retune the window when evidence shows the valid band has moved, narrowed, widened, split, or disappeared.

decision_rules: Do not optimize on target yield alone; require a paired non-target burden measure before expanding intensity.

decision_rules: Treat increases in strictness, dose, energy, confidence, or enforcement as experiments until the upper selectivity bound is known.

decision_rules: Use separate windows for materially different strata when one average window hides subgroup-specific failure.

decision_rules: Add review or abstention zones for ambiguous cases rather than forcing all cases through a brittle binary threshold.

decision_rules: Prefer a narrower validated window when non-target harm is irreversible, hard to observe, or borne by parties outside the decision system.

decision_rules: Retune only with evidence and versioning; do not silently move the window to satisfy short-term throughput pressure.

decision_rules: Exit or pause operation when drift, instrument failure, adversarial adaptation, or case-mix shift makes the current window invalid.

decision_rules: Use a child archetype such as therapeutic-window management only when the selectivity problem is specifically a benefit-harm dose range.

target_invariants: Target and non-target outcomes remain separately visible.

target_invariants: The operating band has both lower and upper bounds rather than a one-sided threshold only.

target_invariants: Bycatch, side effects, and false captures are not hidden behind aggregate success.

target_invariants: Window changes are evidence-backed, documented, and auditable.

target_invariants: Out-of-window conditions trigger hold, review, fallback, or recalibration rather than uncontrolled continuation.

target_invariants: Sensitive contexts preserve procedural fairness, appeal, and non-discrimination safeguards.

target_invariants: Calibration cases include near-boundary and rare high-harm examples.

target_invariants: Operators know which parameter they are actually tuning and what failure modes each direction creates.

expected_outcomes: Higher useful target capture without proportional non-target burden.

expected_outcomes: Lower false-positive, bycatch, side-product, and collateral-harm rates.

expected_outcomes: Fewer failures caused by over-intensifying a process beyond its discriminative range.

expected_outcomes: Clearer rules for when to operate, hold, escalate, or abstain.

expected_outcomes: Improved detection of drift and subgroup-specific window collapse.

expected_outcomes: More defensible tuning decisions in safety-sensitive or legitimacy-sensitive settings.

expected_outcomes: Better separation between discovery experiments, routine operation, and emergency fallback modes.

expected_outcomes: Reduced duplicate drafting pressure around therapeutic windows, threshold tuning, boundary filtering, and bycatch control.

prose_tradeoffs: The main tradeoff is sensitivity versus specificity. Narrower windows reduce collateral capture but may reduce throughput. Wider windows increase coverage but invite bycatch. Static windows support accountability but can go stale. Adaptive windows track drift but can become opaque moving targets. Stratified windows protect heterogeneous contexts but increase complexity and governance burden.

Reaction Selectivity Band Calibration

short_definition: Calibrates temperature, concentration, catalyst, pH, pressure, residence time, or material state so a desired reaction, phase, or property is favored over side products.

distinctive_feature: The control parameter is a physical or chemical condition and the relevant failures are cross-reactivity, side product formation, and loss of material specificity.

distinct_from_parent: The parent is cross-domain; this variant keeps chemistry/materials language and mechanisms.

still_same_parent_because: It still works by mapping and guarding a bounded range where discrimination is preserved.

use_when: A process has desired and undesired products or phases.

use_when: Higher intensity can favor side reactions, decomposition, or reversal.

example_domains:
chemistry
materials science
bioprocessing

domain: chemistry

example: Choosing a temperature band where the desired product forms faster than a side product.

failure_modes:
side reaction dominance
decomposition beyond upper bound
unobserved impurity burden

Classification Selectivity Band

short_definition: Chooses a bounded score, confidence, or strictness range where a classifier separates targets from non-targets without either flooding false positives or missing true cases.

distinctive_feature: The operating parameter is a decision threshold or strictness setting and the failure surface is precision/recall or ROC-shaped.

distinct_from_parent: The parent also covers physical and institutional selectors; this variant is for scored classification and screening.

still_same_parent_because: It retains the same bounded discriminative range and non-target burden logic.

use_when: Classification scores can be thresholded.

use_when: False positives and false negatives both matter.

use_when: There are ambiguous cases that should enter review or abstention.

example_domains:
machine learning
triage
search
moderation

domain: fraud detection

example: Choosing a threshold band with an automatic block zone, review zone, and pass zone.

failure_modes:
overconfident cutoff
subgroup-specific collapse
hidden base-rate drift

Bycatch-Sensitive Selectivity Guarding

short_definition: Treats non-target capture as a primary constraint rather than a side note, narrowing or suspending the operating window when collateral capture rises.

distinctive_feature: The guardrail is organized around bycatch visibility and collateral burden rather than around average target performance.

distinct_from_parent: The parent can be balanced by several metrics; this variant gives priority to non-target harm detection and mitigation.

still_same_parent_because: The intervention is still window calibration around discriminative performance.

use_when: The success metric counts target capture but ignores non-target burden.

use_when: Non-target cases are vulnerable, rare, or costly.

use_when: Operators are tempted to increase intensity because target yield improves.

example_domains:
fisheries
law enforcement
content moderation
medical screening

domain: fisheries

example: Restricting gear settings when non-target capture exceeds a harm-weighted limit.

failure_modes: sacrificing rare non-targets for target yield

Adaptive Selectivity-Window Retuning

short_definition: Continuously or periodically shifts the operating band as target classes, non-target classes, instruments, environments, or adversaries change.

distinctive_feature: Retuning is part of the core operating design rather than a rare maintenance exception.

distinct_from_parent: The parent may use a stable calibrated window; this variant assumes drift is normal.

still_same_parent_because: The object being retuned is still a discriminative operating window.

source_primes: selectivity_window feedback monitoring

use_when: Population mix or material conditions shift.

use_when: Monitoring can detect selectivity drift.

example_domains:
machine learning operations
materials processing
public policy
cybersecurity

domain: ML operations

example: Recalibrating a classifier threshold band after base-rate and adversarial-behavior shifts.

failure_modes:
overfitting the window to noise
undocumented moving target
retuning without stakeholder review

Selective Enforcement Band

short_definition: Sets an enforcement or review intensity band that distinguishes actionable violations from tolerable variation without sweeping in legitimate behavior.

distinctive_feature: The operating parameter is enforcement intensity and the non-target burden is often legitimacy, equity, or access harm.

distinct_from_parent: The parent is not limited to governance or policy enforcement.

still_same_parent_because: It still balances lower misses against upper indiscriminate capture inside a bounded discriminative band.

use_when: Over-enforcement creates collateral harm.

use_when: Under-enforcement leaves target harms unchecked.

example_domains:
platform governance
compliance
public administration

domain: platform governance

example: Choosing a moderation strictness band that removes abuse without suppressing legitimate discussion.

failure_modes:
legitimacy loss
selective enforcement bias
hidden chill effects

near_names:
Selectivity Band Management
Discriminative Operating Window
Specificity Window Calibration
Target/Non-Target Separation Band
Selective Admission Band