Equivalence Class Consolidation

batch: First Iteration/Batch 08

Equivalence Consolidation

notes: Shorter name that points to the same purpose-bound consolidation pattern.

Class Consolidation

notes: Names the class-formation action but should preserve the equivalence criterion and shared-treatment logic.

Same-Treatment Grouping

notes: Emphasizes the downstream consequence of grouping: variants receive the same handling for a defined purpose.

Duplicate Consolidation

notes: Common in records, data, and case-management contexts where superficially distinct items are treated as the same entity or case.

related_primes: representation boundary data_integrity accountability compression invariance idempotence symmetry function relation abstraction equity

slug: isomorphism

slug: representation

slug: compression

slug: invariance

slug: idempotence

slug: symmetry

slug: function

slug: relation

slug: abstraction

components:
Equivalence Criterion
Comparison Scope
Relevant Property Set
Equivalence Class
Shared Treatment Rule
Canonical Representative
Alias Mapping
Exception Rule
Merge/Split Review Path
Consolidation Audit Record
Confidence Level
Class Owner
Merge History
Downstream Propagation Rule
Representative Selection Policy

mechanisms:
Deduplication Workflow
Alias Resolution Table
Canonicalization Pipeline
Identity Resolution Model
Synonym Merge Review
Unit Normalization Table
Master Record Consolidation
Taxonomy Merge Workshop
Policy Equivalence Rule
Equivalence Test Suite
Crosswalk Table

essence_one_line: Treat superficially different entities as equivalent when they share the relevant structure or function, reducing duplication and inconsistent handling.

compression_statement: When variants that should be treated the same are handled separately, define equivalence criteria, group them into classes, and assign shared handling or representation to preserve consistency and reduce duplication while retaining exceptions for relevant differences.

core_intuition: Many systems accumulate surface variation: duplicate records, local names, equivalent credentials, alternate formats, overlapping categories, or interchangeable implementations. Consolidation works by asking which differences matter for the current purpose, grouping items that pass that test, and making the grouping operational through shared treatment and reviewable exceptions.

canonical_formula: equivalence_class_consolidation = comparison_scope + equivalence_criterion + relevant_property_set + equivalence_class + shared_treatment_rule + alias_or_canonical_reference + exception_and_review_path

structural_problem: Multiple items differ superficially but are equivalent for the purpose at hand, causing duplication, inconsistent treatment, fragmented records, avoidable complexity, repeated work, or contradictory decisions.

description: The system accumulates many names, formats, records, statuses, categories, or implementations that should not be handled separately for the current purpose.

description: Equivalent items are processed, counted, reviewed, stored, or governed separately, creating repeated work and inconsistent outcomes.

description: Cases with the same relevant properties receive different decisions because their surface forms differ.

description: A single entity, object, concept, or case is split across records, aliases, systems, or categories, obscuring its full history.

description: Different communities or systems use different terms for the same relevant concept, causing retrieval, reporting, or governance failures.

description: Downstream processes need a shared class, representative, or canonical form to operate consistently.

description: Variants can be used interchangeably for a defined function, but the system has not yet encoded that equivalence.

symptom: The same customer, patient, asset, task, term, or case appears multiple times under different identifiers.

interpretation: The system lacks a stable equivalence criterion and consolidation path.

symptom: Reports double-count or undercount because equivalent records are treated as separate.

interpretation: Class membership and canonical representation are not governed.

symptom: Teams debate whether two categories are different every time a decision arises.

interpretation: Equivalence criteria and exception rules are implicit or contested.

symptom: Search results miss relevant information because synonyms, aliases, and historical names are not connected.

interpretation: Alias mapping has not been integrated into the equivalence class structure.

symptom: Different cases receive different treatment for no relevant reason.

interpretation: Surface distinctions are driving decisions instead of relevant-property equivalence.

symptom: A merge fixes one duplicate but breaks downstream references or histories.

interpretation: Consolidation lacks merge history, audit records, or propagation rules.

symptom: A consolidated category later proves too broad and hides safety, fairness, or performance differences.

interpretation: The equivalence class needs exception handling and boundary review.

root_tension: The intervention must ignore irrelevant differences strongly enough to reduce duplication and inconsistent treatment, but preserve relevant differences strongly enough to avoid false equivalence, unfair grouping, lost meaning, broken history, or unsafe substitution.

case: The main task is assigning items to a taxonomy where the categories are not based on equivalence or shared treatment.

why_not: Use canonical_classification or membership_boundary_refinement when the central intervention is category definition rather than consolidating equivalent variants.

case: The main task is changing multiple forms into one representation while the grouping decision is already obvious.

why_not: Use equivalence_normalization or a normalization mechanism when representation standardization is the core issue, not equivalence-class formation and shared treatment.

case: The main task is choosing an authoritative source among conflicting representations.

why_not: Use source_of_truth_assignment when authority and conflict resolution are primary. Equivalence consolidation can support it but does not establish authority by itself.

case: The main task is translating between two different domains that are not treated as equivalent.

why_not: Use structural_mapping_transfer, boundary_translation, or mapping_reconciliation when cross-domain correspondence is the issue rather than grouping items as same-for-purpose.

case: The differences are genuinely relevant for the decision at hand.

why_not: Do not consolidate. Use equivalence_boundary_review, membership_boundary_refinement, or separate handling to preserve distinctions.

structural_intervention: Define the comparison scope and purpose, specify the relevant equivalence criterion, group items that satisfy it into equivalence classes, choose any necessary canonical representative or alias map, apply a shared treatment rule, and preserve exceptions, audit history, and review paths so consolidation remains correct over time.

logic: Identify what problem consolidation is meant to solve: duplicate work, inconsistent treatment, fragmented identity, search failure, reporting inconsistency, substitutability, or policy same-treatment.

logic: Define which population of items is being compared and which contexts are outside the consolidation decision.

logic: Identify the properties, behaviors, identifiers, meanings, rights, obligations, or outcomes that matter for sameness in this use case.

logic: Convert relevant properties into an operational rule for when two or more items count as equivalent.

logic: Compare candidate items against the criterion, including edge cases and known near-misses, before assigning them to a class.

logic: Group all items that satisfy the criterion into a class and preserve enough membership evidence to justify the grouping.

logic: Specify what happens because of the grouping: shared processing, one count, one decision rule, common routing, same eligibility, or common representation.

logic: Where a single outward-facing form is needed, choose a canonical representative and map aliases, duplicates, local names, or historical forms to it.

logic: Document cases that appear equivalent but are excluded, and mark uncertain or disputed equivalence judgments.

logic: Update downstream systems, preserve merge history, and schedule review or appeal paths so classes can be split, merged, or corrected as conditions change.

description: Only consolidate for a stated purpose; two items can be equivalent for one decision and non-equivalent for another.

description: Any difference that changes safety, fairness, legality, meaning, performance, or downstream behavior must block or qualify consolidation.

description: A consolidation should specify what common handling follows from class membership; otherwise the grouping may be only descriptive.

description: When prior identity, history, source, or local meaning matters, preserve aliases and merge history rather than deleting them.

description: False equivalence risks should be made visible through exception rules, confidence levels, and review paths.

description: Apply consolidation to dependent systems deliberately; uncontrolled propagation can erase relevant distinctions while under-propagation leaves inconsistency.

description: Class membership remains tied to the purpose for which equivalence was defined.

description: Differences that affect safety, fairness, legality, meaning, or downstream behavior are not erased by consolidation.

description: Items inside the same class receive the same defined treatment unless an explicit exception applies.

description: Prior names, records, identifiers, or categories remain traceable where history, audit, or interpretation requires them.

invariant: reviewability

description: The equivalence criterion and class membership can be challenged, audited, split, or updated.

description: The system reduces unnecessary duplicates without losing information needed for valid decisions.

outcome: Reduced duplicate work

description: Equivalent items no longer require repeated review, processing, storage, or decision making.

outcome: More consistent treatment

description: Cases that share relevant properties receive the same handling across systems and actors.

outcome: Cleaner retrieval and reporting

description: Aliases, variants, and duplicates resolve to a shared class so search, counting, and reporting improve.

outcome: Lower coordination burden

description: Teams can rely on shared equivalence classes instead of renegotiating sameness repeatedly.

outcome: Safer substitutability

description: Variants can be substituted only where the equivalence criterion and exception rules support that treatment.

outcome: Improved governance of variants

description: The system can add, split, merge, and review classes without uncontrolled sprawl.

prose_essence:
Equivalence Class Consolidation treats superficially different things as the same **for a defined purpose**. The archetype is not a claim that the things are identical in every respect. It is a governed decision that the differences being ignored do not matter for a particular treatment, count, process, rule, representation, or substitution.

The practical move is simple but powerful: define the equivalence criterion, group variants that satisfy it, and then make the grouping operational through shared handling, canonical representation, alias mapping, exception rules, and review paths. This reduces duplicate work and inconsistent treatment while preserving the ability to recover relevant differences.

prose_when_to_use:
Use this archetype when a system is burdened by variants that should not be handled separately: duplicate records, synonymous terms, equivalent credentials, interchangeable parts, alternate encodings, duplicate cases, local names, or behaviorally equivalent implementations.

It is especially useful when inconsistent treatment has become visible. For example, two records refer to the same entity but receive different decisions, two terms refer to the same process but fragment search, or two input formats trigger different downstream logic even though they should produce the same result.

Do not use it when the differences matter for the decision at hand. The phrase “equivalent for this purpose” is essential. If the purpose changes, the class may need to split.

prose_structural_problem:
The structural problem is variant sprawl without governed same-treatment. Multiple items differ in surface form, location, name, identifier, source, format, or history, but share the relevant structure or function for a current purpose. Because this equivalence is not encoded, the system duplicates work, produces inconsistent decisions, loses history, double-counts, or treats aliases as separate realities.

The root tension is between compression and fidelity. Consolidation compresses many variants into one class, but the compression is only valid if it does not erase differences that matter.

prose_intervention_logic:
The intervention begins by stating the consolidation purpose. A class built for search may differ from a class built for legal eligibility, safety substitution, or statistical analysis. Next, the system bounds the comparison scope and identifies relevant properties: identity, function, meaning, rights, obligations, behavior, measurements, outputs, or decision consequences.

Those properties become an equivalence criterion. Items that satisfy the criterion are grouped into equivalence classes. The system then defines the shared treatment rule: one count, one representative, one routing path, one eligibility rule, one processing behavior, or one substitution class. Finally, it records aliases, exceptions, uncertainty, merge history, and review paths so the class remains governable over time.

prose_key_components:
### Equivalence Criterion

Defines the relevant condition under which different entities count as the same for the current purpose. This is the heart of the archetype. Consolidation is safe only when the criterion states what sameness means and what differences are intentionally ignored.

### Comparison Scope

Specifies the population of items, records, cases, terms, identities, inputs, or situations being compared for possible consolidation. A clear scope prevents the archetype from overreaching. Equivalence inside one registry, policy, dataset, or decision context may not hold outside it.

### Relevant Property Set

Identifies which properties must match, correspond, or be functionally interchangeable for items to share a class. This component distinguishes relevant sameness from superficial resemblance. It also makes visible which properties were excluded from the criterion.

### Equivalence Class

Groups all items that satisfy the equivalence criterion so they can be reasoned about, counted, governed, or handled together. The class is not merely a label. It is the structural unit that allows shared treatment, deduplication, canonical representation, or substitution.

### Shared Treatment Rule

Defines what changes after consolidation: common processing, common decision rules, common permissions, common counting, common naming, or common governance. Without a shared treatment rule, equivalence classification may be descriptive but not yet an intervention. Consolidation should alter handling in a governed way.

### Canonical Representative

Selects or creates the representative name, record, identifier, format, exemplar, or form used when the class needs a single outward-facing representation. A canonical representative is useful but not always required. The archetype is broader than canonical naming because it concerns shared treatment, not only labels.

### Alias Mapping

Connects noncanonical names, duplicate records, local variants, synonyms, encodings, or superficial forms to the equivalence class or canonical representative. The reconciliation controls classify alias_mapping as a component or mechanism, not a standalone archetype. Here it supports discoverability and safe transition.

### Exception Rule

States when apparently equivalent items must not be consolidated because a difference is relevant for safety, fairness, legality, meaning, or downstream behavior. Exception rules reduce false equivalence. They make the ignored-difference policy explicit and create a place to handle edge cases.

### Merge/Split Review Path

Provides a route for challenging a class, adding a variant, splitting an overbroad class, or merging classes that later prove equivalent. Equivalence classes age. Review paths keep consolidation reversible and correctable when new evidence or new use contexts make differences matter.

### Consolidation Audit Record

Records why items were grouped, which criterion was applied, who approved the grouping, and how downstream systems should interpret the consolidation. The audit record preserves accountability and helps downstream users understand whether a consolidation is still valid for their purpose.

prose_common_mechanisms:
The following mechanisms often implement Equivalence Class Consolidation. Each is a tool, artifact, workflow, or procedure; none should be mistaken for the whole archetype.

### Deduplication Workflow

This is a **workflow** that implements the archetype by doing the operational work of consolidation. Finds records, cases, accounts, files, tasks, or objects that satisfy a duplicate criterion and consolidates them into one class or representative record. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Alias Resolution Table

This is a **artifact** that implements the archetype by doing the operational work of consolidation. Lists alternate names, spellings, identifiers, codes, or labels and maps them to a shared equivalence class or canonical representative. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Canonicalization Pipeline

This is a **software_or_tool** that implements the archetype by doing the operational work of consolidation. Transforms equivalent input forms into a normalized class or canonical form before downstream processing, reporting, or governance. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Identity Resolution Model

This is a **method** that implements the archetype by doing the operational work of consolidation. Uses evidence such as identifiers, attributes, histories, behavior, or context to infer when multiple records or names refer to the same entity. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Synonym Merge Review

This is a **procedure** that implements the archetype by doing the operational work of consolidation. Reviews terms that may refer to the same concept and consolidates them when their distinction is not relevant for the target use case. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Unit Normalization Table

This is a **template** that implements the archetype by doing the operational work of consolidation. Maps measurement variants, units, encodings, or formats to a common class or comparable representation for consistent treatment. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Master Record Consolidation

This is a **workflow** that implements the archetype by doing the operational work of consolidation. Merges duplicate or equivalent records into a governed master record while preserving aliases, history, source references, and exceptions. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Taxonomy Merge Workshop

This is a **ritual** that implements the archetype by doing the operational work of consolidation. Brings domain experts together to decide whether categories, terms, codes, or cases should be grouped, split, or treated as near-equivalent. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Policy Equivalence Rule

This is a **document** that implements the archetype by doing the operational work of consolidation. States that several statuses, credentials, cases, or conditions should receive the same administrative or legal treatment for a specified purpose. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Equivalence Test Suite

This is a **test_or_assessment** that implements the archetype by doing the operational work of consolidation. Checks whether variants still produce the same required behavior, output, eligibility, or decision result under the consolidation rule. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

### Crosswalk Table

This is a **artifact** that implements the archetype by doing the operational work of consolidation. Maps codes, labels, schema fields, jurisdictions, or historical categories into consolidated equivalence classes for translation or reporting. It should not be confused with the archetype itself; the archetype is the broader pattern of defining equivalence, grouping variants, assigning shared treatment, and preserving exceptions.

prose_failure_modes:
The central failure mode is false equivalence: variants are grouped even though their differences matter. This can create unfair treatment, unsafe substitution, bad research conclusions, broken records, or invalid decisions.

Another failure mode is history erasure. A merge may simplify the present while destroying old names, references, or records that are needed for audit and interpretation.

Under-consolidation is also possible. Equivalent variants remain separate because no one owns the class or because the criterion is never operationalized.

A subtler failure mode is mechanism capture: teams treat a deduplication tool, alias table, or canonicalization script as if it proves equivalence. Mechanisms can suggest or implement consolidation, but the criterion still needs justification.

prose_neighbor_distinctions:
Equivalence Class Consolidation is distinct from **equivalence_normalization**. Normalization changes or standardizes form; consolidation decides which variants belong together for shared treatment. Normalization is often a mechanism after the equivalence judgment has already been made.

It is distinct from **canonical_classification**. Classification creates categories; consolidation collapses variants into a same-for-purpose class and changes handling.

It is distinct from **membership_boundary_refinement**. Boundary refinement decides whether an item belongs in a category. Consolidation decides whether multiple items or forms should be treated as one class.

It is distinct from **source_of_truth_assignment**. A source of truth establishes authority. Consolidation may identify duplicates or aliases, but it does not by itself decide which system has authoritative update rights.

It is distinct from **relation_mapping** because the relation at issue is not merely visible association; it is equivalence with operational consequences.

prose_cross_domain_examples:
In health records, duplicate patient records may be consolidated after verifying identifiers and clinical history, while preserving previous record IDs for audit.

In knowledge management, different names for the same internal process can resolve to one canonical glossary entry while aliases remain searchable.

In public administration, several credentials may be treated as equivalent for eligibility if their assessment standards match the policy criterion.

In software systems, legacy codes and spelling variants may map into one class before business rules execute.

In supply chains, vendor part numbers may be grouped as substitutes only for assemblies where the relevant safety and performance properties match.

In research synthesis, measurement variants may be grouped for analysis only when they measure the same construct at the required level of precision.

prose_non_examples:
A taxonomy that adds new categories without merging variants is not this archetype. That is classification or boundary design.

A format conversion that rewrites dates or units without a governed same-treatment class is not this archetype. That is normalization or translation.

Choosing an official database without resolving duplicate or equivalent records is not this archetype. That is source-of-truth assignment.

Calling two cases “similar” without defining the relevant criterion, shared treatment, and exceptions is not this archetype. Similarity is not enough; the intervention requires governed equivalence.

prose_tradeoffs:
Consolidation trades nuance for consistency. That is often a good trade when the ignored distinctions are irrelevant, but dangerous when convenience hides meaningful differences.

It also trades local autonomy for shared governance. A canonical class helps coordination, but may conflict with local language, identity, or history.

Automation can make consolidation scalable, especially in deduplication and identity resolution, but it increases the need for confidence levels, audit trails, and appeal paths.

prose_invariants_to_preserve:
The key invariant is purpose-specific sameness: items in the class must remain equivalent for the stated purpose. This does not imply universal identity.

Relevant differences must be preserved. If a difference changes safety, legality, fairness, meaning, performance, or downstream behavior, the class must split or the difference must become an explicit exception.

History must remain recoverable where old references, local names, prior records, or source evidence matter. A consolidation that makes the past unreadable creates a new integrity problem.

Shared treatment must remain consistent. If some systems treat the variants as one while others still treat them separately, the consolidation has not stabilized the system.

prose_target_outcomes:
A successful consolidation reduces duplicate work, duplicate records, duplicate counting, and duplicate decisions. It also improves consistency because same-for-purpose cases receive same-for-purpose treatment.

It improves retrieval and reporting by connecting aliases and variants to a shared class. It improves governance by turning implicit “these are basically the same” judgments into explicit criteria, records, and review paths.

In substitutability contexts, it enables safer replacement: variants can be swapped only when they satisfy the relevant equivalence criterion.

prose_parameter_dimensions:
The most important tuning dimension is **strictness of the equivalence criterion**. A strict criterion reduces false merges but may leave too many duplicates. A loose criterion reduces sprawl but increases false equivalence risk.

A second dimension is **scope**. A class may be valid within one dataset, jurisdiction, workflow, release, policy, or research question and invalid elsewhere. Good drafts specify the boundary of validity.

A third dimension is **granularity**. Consolidating at the level of individual records, concepts, categories, behaviors, households, accounts, vendors, or units changes both the benefits and the risks.

Other important dimensions include confidence thresholds, automation versus human review, canonical representative selection, alias preservation, exception visibility, review cadence, and downstream propagation strength.

prose_variants_and_near_names:
Important variants include duplicate record consolidation, synonym or alias consolidation, same-treatment policy grouping, and behavioral equivalence consolidation. These variants share the parent structure but emphasize different kinds of sameness.

Near names include equivalence consolidation, class consolidation, same-treatment grouping, alias merging, canonicalization, and identity resolution. Some of these are aliases, some are mechanisms, and some are promotion candidates. The variant policy is to keep mechanisms such as alias tables and deduplication workflows inside the parent unless they show distinct cross-domain intervention logic.

`many_to_one_normalization` remains a second-wave candidate. It may be promoted later if the mapping operation itself proves distinct from consolidation and representation normalization.

Duplicate Record Consolidation

short_definition: Consolidates multiple records that refer to the same entity, case, transaction, or artifact into a governed shared record or class.

distinctive_feature: The variant is record-centered and usually requires merge history, source preservation, and downstream propagation rules.

distinct_from_parent: The parent covers any purpose-bound equivalence class; this variant focuses on records that refer to the same underlying entity or case.

still_same_parent_because: The core intervention remains defining an equivalence criterion and applying shared treatment to the consolidated class.

use_when: The same entity or case appears under multiple records or identifiers.

use_when: Duplicate handling causes inconsistent decisions, repeated work, or fragmented accountability.

use_when: Historical references must remain recoverable after the merge.

Equivalence Criterion

role: Defines when records count as duplicates.

Merge History

role: Preserves prior identities and record references.

Canonical Representative

role: Identifies the surviving or master record.

Deduplication Workflow

mechanism_type: workflow

Identity Resolution Model

mechanism_type: method

example: Two patient records are merged after verifying identifiers, history, and exceptions.

example: Multiple accounts belonging to the same customer are consolidated to prevent inconsistent service decisions.

failure_modes: False merge combines distinct entities and causes harmful shared treatment.

failure_modes: Merge history is lost, breaking old references and auditability.

Synonym or Alias Consolidation

short_definition: Treats multiple names, spellings, terms, labels, or identifiers as equivalent for retrieval, governance, or communication.

distinctive_feature: The variant is name-centered: the main work is connecting terms without erasing meaning-bearing distinctions.

distinct_from_parent: The parent can consolidate cases, records, categories, units, or behaviors; this variant consolidates naming variants.

still_same_parent_because: It still depends on an equivalence criterion, alias mapping, exception handling, and shared treatment.

use_when: Different actors use different names for the same concept, resource, entity, or rule.

use_when: Search, reporting, retrieval, or communication fragments because aliases are not connected.

use_when: A canonical name is needed while preserving local or historical names.

Alias Mapping

role: Maps terms to the shared class.

Canonical Representative

role: Provides the preferred name or identifier.

Exception Rule

role: Prevents false synonym merges.

Alias Resolution Table

mechanism_type: artifact

Synonym Merge Review

mechanism_type: procedure

Taxonomy Merge Workshop

mechanism_type: ritual

example: Several labels for the same internal process are mapped to one canonical concept while preserving aliases for search.

example: Regional product names are consolidated under a shared item identity for reporting.

failure_modes: A near-synonym is treated as exact and important contextual meaning is erased.

failure_modes: Canonical naming becomes a political or authority dispute rather than a practical equivalence decision.

Same-Treatment Policy Grouping

short_definition: Groups different cases, statuses, credentials, risks, or conditions so they receive the same decision rule for a specified policy purpose.

distinctive_feature: The variant makes the downstream decision rule central; equivalence is justified by equal treatment under a policy purpose.

distinct_from_parent: The parent includes technical and representational consolidation; this variant is governance and rule-application oriented.

still_same_parent_because: It is still a consolidation into equivalence classes for shared treatment.

use_when: Separate classes produce inconsistent treatment even though their relevant properties match.

use_when: A rule needs to say which variants count as equivalent for eligibility, compliance, access, or remedy.

use_when: Exceptions must be explicit because false equivalence can create unfair or unsafe outcomes.

Shared Treatment Rule

role: States the policy consequence of grouping.

Exception Rule

role: Identifies cases that should not receive same treatment.

Merge/Split Review Path

role: Lets affected parties challenge grouping.

Policy Equivalence Rule

mechanism_type: document

domain: credentialing

example: Several equivalent certifications are grouped as satisfying the same eligibility requirement.

example: Multiple household situations are treated equivalently for benefit eligibility because the relevant resource constraint is the same.

failure_modes: The grouping conceals relevant differences and creates unfair treatment.

failure_modes: Appeal paths are absent, so affected parties cannot correct a false equivalence.

Behavioral Equivalence Consolidation

short_definition: Consolidates variants that produce the same relevant behavior, output, or decision effect even if their internal form differs.

distinctive_feature: Sameness is defined by functional behavior rather than identity, name, or format.

distinct_from_parent: The parent covers any equivalence purpose; this variant uses behavior or output equivalence as the criterion.

still_same_parent_because: It still groups variants into classes and assigns shared treatment under an explicit equivalence criterion.

use_when: Different implementations, processes, or roles produce equivalent observable behavior for a target purpose.

use_when: The system needs substitutability or common testing without caring about internal differences.

use_when: Relevant behavior can be verified through tests or acceptance criteria.

Relevant Property Set

role: Defines which behavior counts as relevant.

Equivalence Test Suite

role: Verifies behavioral sameness through tests.

Shared Treatment Rule

role: Allows substitution or common handling.

example: Two implementations are grouped as equivalent because they pass the same behavior contract for supported inputs.

domain: operations

example: Two fulfillment workflows are treated as interchangeable because they produce the same service outcome under defined conditions.

failure_modes: Outputs match on common cases but diverge on edge cases not included in the equivalence test.

failure_modes: Internal differences matter for safety, maintainability, or equity but are ignored by the behavior criterion.

near_names:
Equivalence Consolidation
Class Consolidation
Same-Treatment Grouping
Alias Merging
Canonicalization
Identity Resolution


Equivalence Normalization

type: archetype

source: generated

batch: First Iteration/Batch 07/Wave 2

Normalization

slug: normalization

Canonicalization

slug: canonicalization

Deduplication

slug: deduplication

related_primes: set_and_membership relation schema function data_integrity interoperability

slug: schema

slug: interoperability

components:
Equivalence Rule
Normalization Scope
Canonical Form
Alias Mapping
Equivalence Class Registry
Normalization Policy
Semantic Preservation Check
Provenance Retention Rule
Exception and Dispute Path
Revision and Version Policy
Confidence Score
Bidirectional Crosswalk
Lossiness Budget
Authority Reference

mechanisms:
Data Normalization
Alias Resolution Table
Unit Conversion Table
Canonicalization Pipeline
Identity Resolution Workflow
Deduplication Workflow
Schema Crosswalk
Synonym Dictionary
Normalization Test Suite
Manual Mapping Review Board

essence_one_line: Normalize superficially different forms that are structurally or functionally equivalent so they can be treated consistently.

compression_statement: When equivalent entities, names, units, records, paths, or representations appear different and fragment action or reasoning, define the relevant equivalence rule and convert, map, link, or register variants under a canonical form or equivalence class.

core_intuition: Many systems fail because they confuse difference of appearance with difference of kind. Equivalence Normalization does not deny all differences; it declares which differences do not matter for a stated purpose, then gives the system a stable way to act on that sameness.

canonical_formula: surface variants + declared equivalence rule + scoped canonical form or mapping → consistent treatment with retained provenance

structural_problem: A system encounters multiple forms, names, units, records, paths, schemas, or cases that are equivalent for the relevant purpose but are treated inconsistently because their surface representations differ.

condition: Different inputs refer to the same entity, concept, measure, case, or action target.

why_it_matters: Without normalization, the system duplicates work, fragments history, or applies inconsistent rules to the same underlying thing.

condition: Equivalent records or forms must be compared, joined, deduplicated, aggregated, searched, or governed.

why_it_matters: Comparison and aggregation fail when each form is treated as a separate object rather than a variant of a shared object.

condition: Local naming, formatting, units, or schemas vary across departments, tools, jurisdictions, languages, sources, or time periods.

why_it_matters: Local variation is often useful at the source but harmful when downstream action requires one common representation.

condition: Surface differences cause inconsistent policy decisions, reporting counts, routing, payments, risk assessments, or user experiences.

why_it_matters: The cost is not cosmetic: the same case can receive different treatment only because it arrived in a different form.

condition: A canonical form already exists but the rules for mapping variants into it are implicit, stale, or contested.

why_it_matters: Implicit normalization hides value judgments, loses provenance, and makes errors difficult to dispute or repair.

symptom: Duplicates appear under slightly different names, identifiers, spellings, formats, or units.

diagnostic_hint: Ask whether the duplicates share the same relevant identity or function under a declared scope.

symptom: Search, reporting, or analytics miss cases because equivalent variants are not linked.

diagnostic_hint: Look for synonyms, aliases, code changes, schema drift, and unconverted units.

symptom: Downstream rules produce inconsistent results for cases that domain experts consider equivalent.

diagnostic_hint: The equivalence rule is probably absent, under-specified, or applied unevenly.

symptom: Integration work repeatedly creates one-off mapping fixes that are not reused.

diagnostic_hint: The system may need a governed equivalence registry instead of ad hoc transformations.

symptom: People disagree about whether two forms are the same, and there is no review route for ambiguous mappings.

diagnostic_hint: Normalization needs an exception and dispute path, not just a conversion script.

root_tension: The system needs a common representation to coordinate action, but the source variation may carry real meaning, history, ownership, or context that should not be erased casually.

case: The items are different because they belong to different categories, not because they are equivalent variants.

why_not_this: Canonical Classification or taxonomy governance is closer because the problem is category assignment, not equivalence normalization.

case: The problem is only that items appear in different sequence.

why_not_this: Canonical Ordering is closer because the intervention stabilizes order without deciding that different forms are equivalent.

case: The issue is which source has authority when conflicting versions disagree.

why_not_this: Source-of-Truth Assignment is closer because the central decision is authority, not equivalence among variants.

case: The goal is to transfer a model or solution from one structurally similar system to another.

why_not_this: Structural Mapping Transfer is closer because it maps relations between different systems rather than normalizing variants of the same target.

case: The transformation simply cleans formatting without changing treatment, identity, comparison, or governance.

why_not_this: That is a local mechanism or hygiene step, not the full archetype.

structural_intervention: Declare the relevant equivalence relation, define the scope and canonical representation, map or convert variants into that representation, validate meaning preservation, retain provenance, and govern exceptions and rule changes.

step: Define the normalization purpose.

logic: Name the decision, comparison, aggregation, search, routing, or governance action that requires equivalent forms to be treated consistently.

step: Identify the variant universe.

logic: List the forms, names, units, schemas, records, paths, or cases that may represent the same underlying target.

step: State the equivalence rule.

logic: Define which features must match, which differences are irrelevant, and which differences block equivalence under the chosen purpose.

step: Choose the canonical form or class representation.

logic: Select the standard representation, identifier, unit, label, schema field, or equivalence-class key used for downstream action.

step: Build the mapping or conversion path.

logic: Create mappings, transformations, aliases, crosswalks, or conversion procedures from source forms to canonical forms.

step: Validate semantic preservation.

logic: Test whether the normalized result preserves meaning and actionability for common, edge, ambiguous, and high-stakes cases.

step: Retain provenance and reversibility where needed.

logic: Preserve source form, mapping rationale, confidence, version, and transformation history when future audit or dispute may matter.

step: Route exceptions and contested cases.

logic: Do not silently normalize novel, ambiguous, conflicting, or high-impact cases; route them to review, source-of-truth assignment, or explicit non-equivalence.

step: Version and maintain the mapping.

logic: Update equivalence rules and canonical forms deliberately as meanings, schemas, authorities, or contexts change.

rule: Normalize only for a declared purpose and scope.

purpose: Prevents a useful local equivalence from being treated as universal sameness.

rule: Require an explicit equivalence criterion before collapsing variants.

purpose: Prevents arbitrary similarity, convenience, or surface resemblance from becoming an unreviewed merge.

rule: Preserve source distinctions when they may affect rights, safety, interpretation, audit, or future action.

purpose: Prevents consistency gains from erasing consequential context.

rule: Prefer mapping over destructive conversion when equivalence is contextual or contested.

purpose: Allows common treatment without forcing irreversible collapse.

rule: Escalate ambiguous mappings rather than making silent defaults authoritative.

purpose: Keeps contested equivalence decisions reviewable and accountable.

rule: Version normalization rules when downstream artifacts depend on historical interpretation.

purpose: Prevents old decisions from being reinterpreted under new equivalence logic without trace.

Same Relevant Thing, Same Treatment

description: Variants that meet the equivalence rule receive consistent downstream action inside the declared scope.

Meaning Preservation

description: Normalization does not erase distinctions that matter for the decision, safety constraint, right, obligation, or interpretation at stake.

Scope-Limited Equivalence

description: Equivalence is declared for a purpose and does not become a universal claim that all differences are irrelevant.

Traceable Mapping

description: A reviewer can reconstruct how a source form became a canonical form and why that mapping was valid.

Exception Visibility

description: Ambiguous, contested, and high-impact cases are visible rather than silently forced through the normal path.

Versioned Equivalence Logic

description: Changes to equivalence rules, canonical forms, and mappings are dated, reviewable, and interpretable across time.

Reduced Duplicate Handling

description: Equivalent variants no longer create duplicate work, records, cases, charges, analyses, or identities.

Consistent Downstream Action

description: Rules, workflows, reports, and decisions operate on the normalized target rather than surface variation.

Improved Interoperability

description: Systems with different labels, schemas, units, or record structures can exchange meaning more reliably.

Better Search and Retrieval

description: Users can find related material despite synonyms, aliases, code changes, formatting variation, or historical terms.

Auditability of Sameness Decisions

description: The system can explain when and why two forms were treated as equivalent or not equivalent.

prose_essence:
Equivalence Normalization is the intervention of deciding when different-looking forms should count as the same for a particular purpose, then giving the system a stable way to act on that sameness. It is not just cleaning data, choosing a preferred name, or sorting items into a tidy order. It is a governed sameness decision: these forms are different on the surface, but for this action they should be treated as equivalent.

The archetype becomes powerful when representation variation is causing fragmentation. One record is spelled three ways. One measurement appears in several units. One policy status has local office names. One product, patient, client, asset, or case is duplicated because identifiers changed. Without normalization, the system performs duplicate work and inconsistent reasoning. With normalization, the system can compare, aggregate, route, search, audit, or decide consistently.

prose_when_to_use:
Use this archetype when equivalent variants are being treated as different because their names, formats, paths, units, schemas, labels, or records differ. The strongest trigger is repeated inconsistency: the same relevant thing receives different treatment depending on how it enters the system.

It is especially apt when downstream action depends on consistency. A search index needs to retrieve all aliases. A workflow engine needs one status vocabulary. A dataset needs comparable units. A case-management process needs duplicate records linked. A policy office needs equivalent local terms mapped to shared reporting categories.

Do not use it merely because you want tidier formatting. Formatting cleanup becomes Equivalence Normalization only when it encodes an equivalence rule that changes treatment, comparison, retrieval, aggregation, or governance.

prose_structural_problem:
The structural problem is fragmentation by representation. A system sees many surface forms and treats them as separate even when they should be the same for the relevant purpose. The result can be duplicate records, missed search results, inconsistent eligibility decisions, broken joins, conflicting reports, duplicate payments, mismatched histories, or unsafe comparisons.

The hard part is that surface variation is not always noise. Local terms, source systems, historical codes, cultural names, legal labels, and measurement units may carry information that matters in some contexts. Equivalence Normalization therefore has to avoid two opposite errors: false splits, where equivalent variants remain separate, and false merges, where meaningful differences are erased.

prose_intervention_logic:
The intervention starts by naming the purpose of normalization. Equivalent for what? Search, reporting, eligibility, routing, aggregation, safety checking, billing, auditing, translation, or comparison? The answer determines which differences matter.

Next, the system defines an equivalence rule. This rule states when two variants should be treated as the same and when they must remain distinct. The rule then points to a canonical form, common unit, shared identifier, equivalence class, mapping table, or crosswalk. Source variants are converted, mapped, linked, or registered under that representation.

A mature implementation also validates semantic preservation. The normalized form must still mean what downstream action needs it to mean. When the conversion is lossy, ambiguous, high-stakes, or contested, the system should retain provenance and route the case to review rather than silently force it into the normal path.

prose_key_components:
### Equivalence Rule

The equivalence rule is the heart of the archetype. It states when two forms, records, units, labels, paths, or entities count as the same for the current purpose. Without this rule, normalization becomes guesswork or convenience. A good rule identifies required matches, irrelevant differences, blocking differences, and evidence thresholds.

### Normalization Scope

Normalization scope keeps sameness from becoming overreach. Two forms may be equivalent for reporting but not for clinical action, legal interpretation, payment, identity proofing, or safety review. Scope says where the equivalence applies and where local distinctions remain legitimate.

### Canonical Form

The canonical form is the common representation produced by the normalization process. It may be a preferred label, standard identifier, common unit, internal schema, normalized status, or equivalence-class key. It should be stable enough for downstream use but not so rigid that it cannot evolve.

### Alias Mapping

Alias mapping links alternate names, spellings, codes, abbreviations, paths, or source-specific forms to the canonical representation. It preserves retrieval and interoperability while preventing alternate forms from becoming duplicate targets of action.

### Equivalence Class Registry

The registry records which variants are currently treated as equivalent. It turns invisible sameness decisions into auditable knowledge. It helps future reviewers inspect why a mapping exists, when it changed, which source forms belong to the class, and whether the class has drifted.

### Normalization Policy

The normalization policy explains how mappings are created, approved, applied, revised, and disputed. It distinguishes automatic cases from cases needing review. Without policy, a normalization table can become an ungoverned authority.

### Semantic Preservation Check

The semantic preservation check asks whether the normalized form still preserves the meaning needed by downstream action. This matters when different units imply precision limits, when legal terms only partially overlap, or when a preferred label hides context.

### Provenance Retention Rule

The provenance rule decides what original information remains attached to the normalized result. It may preserve source form, source system, confidence, mapping rationale, timestamp, and rule version. Provenance lets people audit, debug, reverse, or dispute normalization decisions.

### Exception and Dispute Path

Some cases should not be normalized automatically. Ambiguous matches, conflicting sources, new terms, high-impact identity merges, and contested legal meanings need an exception path. This path protects the system from silent false equivalence.

### Revision and Version Policy

Equivalence logic changes. Names change, schemas evolve, standards update, and local meanings drift. A revision and version policy makes these changes explicit so historical artifacts can still be interpreted under the rules that produced them.

prose_common_mechanisms:
### Data Normalization

Data normalization converts data fields, formats, or structures into standard forms. It implements the archetype when the conversion is governed by an equivalence rule and affects comparison, integration, or action. It is not the archetype by itself; it is one common implementation mechanism.

### Alias Resolution Table

An alias resolution table maps alternate names or identifiers to canonical references. It is useful for search, registries, product catalogs, legal references, knowledge bases, and customer records. The table should carry enough evidence and provenance to avoid arbitrary merging.

### Unit Conversion Table

A unit conversion table implements quantitative equivalence. It converts different units into a common measure while preserving precision, rounding, assumptions, and validity conditions. Unit conversion is simple only when the equivalence is exact and context-free; many real cases require care.

### Canonicalization Pipeline

A canonicalization pipeline automates parsing, cleaning, mapping, conversion, validation, and exception routing. It is useful at scale but can become dangerous if it silently collapses ambiguous cases. The pipeline should include tests, logs, and review thresholds.

### Identity Resolution Workflow

Identity resolution determines whether several records or references point to the same entity. It often combines rules, probabilistic matching, manual review, and merge governance. It instantiates the archetype when it declares and manages equivalence among records or identities.

### Deduplication Workflow

Deduplication detects and merges or links duplicate records. It should be treated as a mechanism, not the whole archetype. A deduplication workflow without an equivalence rule and provenance can destroy important distinctions.

### Schema Crosswalk

A schema crosswalk maps fields, categories, or codes across different schemas. It supports interoperability when systems do not share the same representation. Crosswalks need explicit handling for exact equivalence, partial equivalence, many-to-one mapping, and non-equivalence.

### Synonym Dictionary

A synonym dictionary groups alternate words or phrases under preferred terms. It can improve search and retrieval while retaining user vocabulary. It should avoid assuming that all synonyms are equivalent in every context.

### Normalization Test Suite

A test suite checks known equivalents, known non-equivalents, edge cases, ambiguous cases, and historical variants. It makes normalization behavior inspectable and helps detect regressions when rules change.

### Manual Mapping Review Board

A review board or review procedure handles cases where automated rules lack enough authority. This mechanism is especially important when normalization affects safety, rights, identity, liability, eligibility, or financial outcomes.

prose_failure_modes:
A false merge occurs when the equivalence rule is too broad and collapses forms that should remain distinct. This can cause safety errors, rights violations, duplicated identity problems, and incorrect reporting.

A false split occurs when equivalent variants remain separate. This creates duplicate records, missed retrieval, duplicate work, and inconsistent decisions.

Semantic loss occurs when normalization discards context that matters later. It is common when many-to-one mappings are irreversible or when local labels carry legal, cultural, clinical, or operational meaning.

Scope creep occurs when a mapping created for one purpose is reused in a context where it does not hold. The fix is explicit scope metadata and review before reuse.

Authority capture occurs when a local canonical form becomes authoritative without accountable approval. It can be mitigated with authority references, dispute paths, and versioned governance.

Stale mapping drift occurs when source terms, schemas, or meanings change but the normalization rule stays frozen. Monitoring unmatched variants and reviewing mappings on a cadence can reduce this failure.

prose_neighbor_distinctions:
Equivalence Normalization differs from **Canonical Ordering** because ordering solves sequence instability. Normalization solves inconsistent treatment of equivalent variants. A system may need both, but the central decision differs.

It differs from **Canonical Classification** because classification assigns items to categories, while normalization identifies variants that should count as the same for a particular purpose. Classification asks “what kind is this?” Normalization asks “is this the same relevant thing under another form?”

It differs from **Canonical Naming and Reference** because naming is one part of equivalence governance. Equivalence Normalization also covers units, schemas, records, paths, statuses, case labels, and structural forms.

It differs from **Source-of-Truth Assignment** because source-of-truth logic chooses which representation has authority when sources conflict. Equivalence Normalization may rely on an authority, but its main task is mapping variants into consistent treatment.

It differs from **Structural Mapping Transfer** because structural transfer moves reasoning between different systems. Equivalence Normalization handles variants within a shared target of action.

It differs from **Data Normalization** because data normalization is usually a mechanism. The archetype is broader: it includes the equivalence rule, scope, canonical form, validation, provenance, exception path, and revision governance.

prose_cross_domain_examples:
In enterprise data, several customer records may refer to the same customer under different identifiers and spellings. Equivalence Normalization links them to one governed identity while preserving source history.

In scientific data, equivalent measurements expressed in different units need conversion before comparison or aggregation. The system should also preserve conversion assumptions and precision limits.

In policy administration, local program labels may refer to the same benefit category. Normalization supports common eligibility and reporting while preserving local terminology for audit.

In knowledge management, synonyms and historical names can be mapped to preferred concepts so users retrieve the full body of relevant material.

In healthcare operations, terminology and unit variants can affect safety. Normalization must therefore be carefully validated, scoped, and auditable.

In API design, external payloads may arrive in several schemas. A canonical internal schema can normalize equivalent input structures before validation and business logic.

prose_non_examples:
Alphabetizing a list is not Equivalence Normalization. It is an ordering mechanism unless the alphabetized representation also maps equivalent forms.

Choosing a preferred category label is not enough. That may be classification or naming unless alternate labels are explicitly mapped as equivalent for action.

Deleting records because they look similar is not a mature use of this archetype. It lacks an explicit equivalence rule, provenance, and dispute path.

Forcing all local terms into a central label when local distinctions affect rights, safety, or interpretation is a misuse. It violates the meaning-preservation invariant.

prose_tradeoffs:
Equivalence Normalization trades local richness for shared consistency. A canonical form helps coordination, but it can hide the source variation that gave a term or record its meaning.

It also trades efficiency for reviewability. Automatic normalization can handle volume, but meaningful review requires provenance, test cases, and exception routes.

Another tradeoff is collapse versus linkage. Sometimes variants should be merged into one canonical representation. In other cases they should remain separate but linked, allowing common treatment without destroying original context.

There is also a stability tradeoff. Stable canonical forms support reproducibility, but terminologies and standards evolve. A mapping that was correct last year may be wrong after a schema, law, or domain standard changes.

prose_invariants_to_preserve:
The core invariant is same relevant thing, same treatment. If two variants satisfy the declared equivalence rule, they should receive consistent downstream handling inside the normalization scope.

A second invariant is meaning preservation. Normalization must not erase distinctions that matter for safety, rights, obligations, eligibility, identity, interpretation, or audit.

A third invariant is scope-limited equivalence. Equivalence for one purpose is not equivalence for every purpose. The mapping should carry its scope so it is not reused blindly.

Traceable mapping is also essential. A reviewer should be able to reconstruct how the source form became the canonical form and why the mapping was considered valid.

Finally, exception visibility must be preserved. Ambiguous or contested cases should be visible, not quietly forced into the nearest standard form.

prose_target_outcomes:
The primary outcome is consistent downstream action. Equivalent forms should no longer trigger inconsistent routing, comparison, reporting, search, eligibility, aggregation, or decision logic.

A second outcome is reduced duplication. Equivalent variants no longer create duplicate records, duplicated work, duplicate charges, duplicate cases, or fragmented history.

A third outcome is improved interoperability. Different systems can exchange meaning without requiring every system to use identical surface forms.

A fourth outcome is better retrieval. Users can find relevant material despite synonyms, aliases, old codes, alternate titles, spelling variants, or local labels.

A fifth outcome is auditability of sameness decisions. The system can explain why two forms were treated as equivalent, not equivalent, or review-needed.

prose_parameter_dimensions:
The strictness of equivalence determines how much evidence is required before variants are treated as the same. Strict rules reduce false merges but leave more duplicates. Permissive rules reduce fragmentation but increase the risk of semantic erasure.

The normalization scope determines where the mapping is authoritative. A narrow scope preserves context; a broad scope improves consistency but can overrule local distinctions.

The canonical form granularity determines how much detail the common representation preserves. Coarse forms are easier to process. Fine-grained forms preserve nuance but may reduce the benefits of normalization.

Lossiness tolerance determines whether source-specific details can be discarded. Low tolerance requires provenance and reversible mappings. High tolerance favors speed and simplicity but is risky in high-stakes contexts.

Automation level determines which mappings can be applied by rule and which require human review. The best systems automate clear cases and escalate ambiguous or consequential cases.

The authority model determines who can define, approve, revise, or dispute equivalence. Central authority improves consistency; distributed authority can preserve local expertise but requires reconciliation.

prose_variants_and_near_names:
**Equivalence Class Consolidation** is a close merge-review variant. It emphasizes grouping variants into equivalence classes. It may deserve separate review if future batches show that membership consolidation is distinct from canonical conversion.

**Many-to-One Normalization** maps multiple input forms to one output form. It is useful but risky because many-to-one mappings can be irreversible.

**Alias Resolution Normalization** handles alternate names or references. It is common in search, records, catalogs, citations, and knowledge bases.

**Unit Normalization** handles values expressed in different units or scales. It needs attention to precision, rounding, and dimensional validity.

**Schema Equivalence Crosswalk** maps fields or codes across systems. It is especially useful when equivalence is partial or contextual rather than exact.

Near names include normalization, canonicalization, data normalization, alias resolution, deduplication, entity resolution, synonym folding, and semantic normalization. These terms should be preserved for retrieval, but they should not all become separate top-level archetypes.

variant_type: subtype

short_definition: Treat superficially different entities as members of the same equivalence class when they share the relevant structure or function.

distinctive_feature: The emphasis is on class membership and consolidation, not only converting each variant to a formatted canonical representation.

distinct_from_parent: Equivalence Normalization includes consolidation but also covers conversion, alias mapping, canonical forms, validation, and versioned governance.

still_same_parent_because: The underlying intervention still identifies relevant sameness and coordinates treatment through a common representation or equivalence class.

use_when: Several records, forms, labels, or cases are being handled separately even though the relevant decision should treat them as the same.

use_when: The main work is grouping variants into equivalence classes before downstream action occurs.

example_domains:
records management
taxonomy governance
case handling
data integration

Many-to-One Normalization

short_definition: Map multiple input forms to one canonical output form so downstream processing becomes consistent.

distinctive_feature: The mapping is explicitly many-to-one and therefore must manage irreversibility or information loss.

distinct_from_parent: The parent also covers one-to-one conversion, equivalence-class labeling, alias linking, and crosswalk mapping without forced collapse.

still_same_parent_because: The core intervention remains declaring relevant equivalence and routing variants to a common representation.

use_when: Inputs arrive in many formats, names, encodings, units, or spellings but one downstream form is needed.

use_when: The system can tolerate losing some source-specific variation or can preserve it as provenance.

example_domains:
data ingestion
unit conversion
form processing
registry governance

failure_modes: Irreversible collapse hides source distinctions that later become important.

Alias Resolution Normalization

short_definition: Resolve alternate names or references to a shared canonical reference while preserving retrieval by the aliases.

distinctive_feature: The variant is mostly about names and references rather than units, schema fields, or structural mappings.

distinct_from_parent: The parent is broader and includes normalization of forms, structures, paths, measures, cases, and records.

still_same_parent_because: Alias resolution still depends on an equivalence rule and a governed canonical form or reference.

use_when: The same entity, concept, person, product, policy, or record appears under several names.

use_when: Users must search or reason across names without creating duplicate objects of action.

example_domains:
knowledge bases
customer records
legal citations
product catalogs

Unit Normalization

short_definition: Convert equivalent quantities expressed in different units into a common unit for comparison, aggregation, or decision.

distinctive_feature: The equivalence rule is often formal and conversion-based, but precision, rounding, and context still matter.

distinct_from_parent: The parent includes non-quantitative equivalence where conversion is semantic, legal, procedural, or structural.

still_same_parent_because: The intervention still maps equivalent forms to a governed common representation.

use_when: Values are mathematically or operationally comparable but expressed in different units or scales.

use_when: Downstream aggregation or threshold checks require a common measure.

example_domains:
engineering
clinical dosing support
finance reporting
scientific data integration

Schema Equivalence Crosswalk

short_definition: Map fields, categories, or codes across different schemas when they represent equivalent or partially equivalent meanings.

distinctive_feature: The crosswalk must handle partial equivalence and non-equivalence rather than assuming every field maps cleanly.

distinct_from_parent: The parent is broader; schema crosswalks are one mechanism-heavy subtype.

still_same_parent_because: The same equivalence-rule and semantic-preservation logic governs whether a mapping is valid.

use_when: Two systems must exchange data but use different field names, category structures, or code systems.

use_when: Some mappings are exact while others are partial, contextual, or contested.

example_domains:
enterprise integration
public-sector reporting
research datasets
standards migration

near_names:
Normalization
Canonicalization
Data Normalization
Deduplication
Alias Resolution
Entity Resolution
Semantic Normalization


batch: Batch 09

source_primes: equivalence_preserving_rewriting transformation equivalence_relation invariance optimization representation refactoring

slug: transformation

slug: optimization

slug: refactoring

slug: verification

slug: validation

slug: algorithm

slug: boundedness

slug: constraint

slug: bijectivity

slug: inversion

slug: contraposition

slug: predicate

slug: order

components:
Equivalence Relation Contract
Source Form and Context
Observable Behavior Boundary
Rewrite Rule Set
Candidate Rewrite Space
Invariant Preservation Oracle
Orthogonal Cost Criterion
Selection and Acceptance Rule
Rewrite Trace and Provenance
Rollback or Backtranslation Path
Canonical or Normal-Form Target
Rewrite Priority Strategy
Confluence and Termination Check
Benchmark or Cost Measurement Panel
Exception and Side-Effect Register
Human Review Boundary

mechanisms:
Semantics-Preserving Refactoring
Compiler Optimization Pass
Query Plan Rewriter
Algebraic Simplification Rulebook
Rewrite System with Confluence Tests
Normal-Form Reduction
Property-Based Equivalence Test
Metamorphic Test Suite
Golden-Output Regression Test
Benchmark Harness
Rewrite Trace Log
Peephole Optimization

essence_one_line: Rewrite something into a cheaper, clearer, faster, safer, or more usable form only after proving or testing that the declared behavior stays equivalent.

compression_statement: Equivalence-Preserving Rewrite Optimization applies when an object can take multiple forms that are equivalent under a named relation but differ operationally. The intervention makes the equivalence relation explicit, defines observable behavior and side-effect boundaries, generates or selects candidate rewrites, verifies preservation, ranks accepted candidates by an orthogonal cost criterion, and records the rewrite trace so the new form can be audited, rolled back, or compared with the old one.

core_intuition: A rewrite is attractive because form matters: one equivalent form may be faster, clearer, easier to prove, easier to maintain, or easier to execute. But the benefit is valid only if equivalence is not smuggled in as an assumption. The pattern separates two questions: does this form preserve the declared behavior, and among the forms that preserve it, which one has the best operational profile?

canonical_formula: Given object x, equivalence relation ~, admissible rewrite relation R, and cost C, choose y such that x R* y, x ~ y, and C(y) is preferable among verified candidates; never optimize C by weakening ~.

structural_problem: A system needs to change form without changing meaning, behavior, obligation, result set, or externally visible contract. Teams often optimize, simplify, refactor, normalize, or translate before specifying what must remain equivalent. This creates hidden semantic drift, broken compatibility, lost side effects, invalid proofs, misleading performance gains, or untraceable differences between old and new forms.

trigger_conditions: An artifact, expression, program, plan, rule, model, workflow, schema, or representation has multiple possible equivalent forms.

trigger_conditions: The current form is costly, slow, brittle, opaque, hard to verify, hard to maintain, or poorly suited to an execution environment.

trigger_conditions: Stakeholders want improvement without changing the promised behavior, interpretation, result, obligation, or interface.

trigger_conditions: Several rewrite rules, transformations, refactorings, simplifications, or plan choices could apply.

trigger_conditions: The improvement criterion is not the same as the equivalence criterion, such as optimizing speed while preserving result semantics.

trigger_conditions: Existing tests, contracts, proofs, benchmarks, or compatibility expectations are insufficient to tell whether the rewrite is safe.

trigger_conditions: The rewrite may affect hidden observables such as ordering, latency, logging, audit traces, failure modes, permissions, or side effects.

trigger_conditions: A later reviewer must know why the new form is considered equivalent and why it was selected.

symptoms: A refactor passes common cases but changes edge-case behavior, timing, side effects, or error semantics.

symptoms: A faster plan returns the same ordinary outputs but violates ordering, permissions, precision, or audit expectations.

symptoms: A simplification rule is applied outside its preconditions.

symptoms: Teams debate whether two forms are equivalent only after the rewrite has already shipped.

symptoms: Optimization work lacks a trace linking each transformation to preservation evidence.

symptoms: A canonical form is selected for convenience even though another equivalent form would be safer or cheaper for the task.

symptoms: A benchmark improvement hides a loss of maintainability, explainability, portability, or reversibility.

symptoms: A translation or migration is treated as harmless because it looks similar, not because a relation was validated.

root_tension: Operational form matters, but changing form threatens meaning. The tension is between exploiting equivalent alternatives for performance, clarity, or control and preserving the behavior, commitments, or semantics that made the original acceptable.

anti_signatures: The task is merely to put data into one consistent representative form for uniform handling; use equivalence_normalization.

anti_signatures: The task is to decide whether different entities should be treated as the same class; use equivalence_class_consolidation.

anti_signatures: The task is to change the sameness criterion itself by splitting or merging classes; use equivalence_relation_refinement_and_coarsening.

anti_signatures: The new form is allowed to approximate the old with bounded error; use bounded_approximation rather than exact rewrite preservation.

anti_signatures: The task is a migration replacement whose main question is old-new overlap validation; use correspondence_validation.

anti_signatures: The task is general cleanup, prioritization, or debt tracking without an explicit equivalence relation; use technical_debt_containment.

anti_signatures: The transformation intentionally changes behavior, incentives, or outcomes; this is redesign, not equivalence-preserving rewriting.

structural_intervention: Convert an informal change-of-form request into a two-gate rewrite process: first declare and verify the equivalence relation for admissible rewrites, then select among verified candidates by a separately stated operational cost criterion. Preserve rewrite provenance, evidence, and rollback/backtranslation paths.

action_logic: Name the source object and the context in which its behavior or meaning must be preserved.

action_logic: Declare the equivalence relation, including outputs, side effects, timing, ordering, failure semantics, legal commitments, interface guarantees, and other observables that matter.

action_logic: Define allowed rewrite rules or transformation families and their preconditions.

action_logic: Generate or enumerate candidate rewrites without accepting them yet.

action_logic: Use proofs, type checks, regression tests, property tests, differential comparison, expert review, or formal equivalence to reject candidates that do not preserve the relation.

action_logic: Define the orthogonal cost criterion: speed, memory, maintainability, clarity, risk, auditability, resource use, portability, or proof burden.

action_logic: Rank only the equivalence-verified candidates by the cost criterion and select according to an acceptance rule.

action_logic: Record the rewrite path, evidence, cost measurement, assumptions, version, and accountable owner.

action_logic: Maintain rollback, backtranslation, comparison, or monitoring paths for cases where the equivalence relation later proves incomplete.

decision_rules: Do not rank a candidate by cost until preservation of the equivalence relation has been checked at the required confidence level.

decision_rules: Do not redefine equivalence after seeing a cheaper candidate unless the change is explicitly reviewed as an equivalence-relation revision.

decision_rules: Treat side effects, timing, ordering, error behavior, permissions, and audit traces as observable unless explicitly excluded.

decision_rules: Use stronger preservation oracles when stakes, coupling, or downstream dependency are high.

decision_rules: Prefer reversible or traceable rewrites when evidence is incomplete or future compatibility is uncertain.

decision_rules: If a rewrite improves one cost dimension while worsening another, use an explicit tradeoff rule rather than calling it simply better.

decision_rules: If no candidate preserves equivalence, stop or reframe the work as behavior-changing redesign.

target_invariants: The declared equivalence relation remains explicit and stable during selection.

target_invariants: The source and target forms preserve required observable behavior within the named context.

target_invariants: Optimization criteria remain subordinate to equivalence preservation.

target_invariants: Rewrite preconditions are checked before rules are applied.

target_invariants: Evidence of preservation is retained with the rewrite trace.

target_invariants: Users or downstream systems that depend on the old form can still interpret, compare, or recover from the new form when required.

target_invariants: Approximation, loss, convention change, and intentional behavior change are not mislabeled as equivalence-preserving rewrite.

expected_outcomes: Safer refactoring, optimization, simplification, and plan selection.

expected_outcomes: Clearer distinction between equivalence preservation and behavior-changing redesign.

expected_outcomes: Reduced semantic drift during performance tuning, migration, compilation, schema translation, and process improvement.

expected_outcomes: More auditable transformations with preserved evidence and rollback paths.

expected_outcomes: Better use of cheaper, clearer, or faster forms without weakening correctness or compatibility.

expected_outcomes: Cleaner boundaries between normalization, canonicalization, correspondence validation, and rewrite optimization.

Semantics-Preserving Refactoring

short_definition: Rewrite internal software structure while preserving externally observable behavior and improving maintainability, modularity, testability, or risk.

distinctive_feature: The equivalence relation is usually defined by public behavior, tests, interfaces, and compatibility expectations rather than by mathematical identity alone.

distinct_from_parent: The parent is cross-domain; this variant focuses on software and process refactoring with technical-debt and regression-test machinery.

still_same_parent_because: It still transforms a form into a behavior-preserving but operationally better form under an explicit equivalence contract.

use_when: The public contract, tests, or observable behavior should remain unchanged.

use_when: The internal representation creates complexity, duplication, coupling, or maintenance risk.

use_when: Improvement is judged by maintainability, clarity, performance, or evolution cost rather than by new functionality.

example: A module is decomposed into smaller services while the API contract and regression suite remain stable.

example: A slow transformation pipeline is reorganized into streaming stages while preserving output records and failure semantics.

failure_modes: Regression tests do not cover side effects or compatibility expectations.

failure_modes: The rewrite improves internal elegance but changes latency, ordering, or failure behavior users relied on.

Algebraic Rewrite Optimization

short_definition: Use valid algebraic, logical, symbolic, or rule-based identities to transform expressions into equivalent forms that are easier to compute, prove, inspect, or compare.

distinctive_feature: The rewrite relation is often rule-system based and may need confluence, termination, and normal-form checks.

distinct_from_parent: The parent includes organizational, software, and representation rewrites; this variant emphasizes formal expression transformation.

still_same_parent_because: It preserves equivalence while selecting among operationally different forms.

use_when: The object has formal rewrite rules with known preconditions.

use_when: Different equivalent expressions have different operational costs or proof difficulty.

use_when: The system needs a traceable sequence of rule applications rather than informal simplification.

example: A constant expression is replaced by its computed value after type and overflow rules are checked.

example: A specification is rewritten into an equivalent normal form that a solver can decide more efficiently.

failure_modes: A rewrite identity is used outside its preconditions.

failure_modes: The rewrite system loops or reaches path-dependent forms with different practical meanings.

Cost-Based Query or Plan Rewriting

short_definition: Generate equivalent execution plans, query forms, or workflow plans, then select the one with better estimated resource cost while preserving result semantics.

distinctive_feature: The candidate space is plan-like and selected by estimated execution cost, often under uncertainty about data shape or runtime environment.

distinct_from_parent: Narrower than the parent because it focuses on executable plans and resource estimates.

still_same_parent_because: The plan rewrite preserves the declared result relation while changing operational behavior.

source_primes: equivalence_preserving_rewriting algorithm optimization representation

use_when: A declarative query, plan, or workflow can be executed through multiple equivalent paths.

use_when: The selected path should improve time, memory, cost, throughput, or resource contention.

use_when: Result equivalence must be established before cost ranking is trusted.

example: A query is reordered to use a selective index first while returning the same result set.

example: A workflow planner executes independent validations in parallel while preserving the same acceptance criteria.

failure_modes: Cost estimates are stale or ignore resource contention.

failure_modes: The plan changes ordering, timing, or side effects that were observably meaningful.

Contrapositive Rewrite for Proof

short_definition: Rewrite an implication into an equivalent contrapositive when the rewritten form is easier to prove, test, or operationalize.

distinctive_feature: It connects the previous queue output on contraposition to the current rewrite archetype by treating contrapositive form as an equivalent representation selected for proof cost.

distinct_from_parent: Narrower because the rewrite class is a logical implication transformation.

still_same_parent_because: It is an equivalence-preserving rewrite chosen for an orthogonal proof or verification advantage.

use_when: The logical system licenses the contrapositive rewrite.

use_when: The contrapositive form is easier to establish than the forward implication.

use_when: The rewrite is used for proof or verification rather than for practical rule-out evidence alone.

example: A safety condition is rewritten into a contrapositive obligation that can be checked against missing required states.

failure_modes: Using the contrapositive in a logic or policy setting where the equivalence does not hold.


name: Equivalence-Relation Refinement and Coarsening

batch: Second Iteration/Batch 05

aliases:
Relation Granularity Tuning
Equivalence Class Split/Merge Design
Dynamic Equivalence Relation Adjustment
Class Resolution Refinement
Equivalence Granularity Management
Membership Relation Recalibration

related_primes: set_and_membership relation boundary classification schema ontology abstraction scale hierarchy compression invariance variability representation cardinality threshold feedback iteration approximation progressive_refinement_from_core_model

slug: refinement

slug: relation

slug: boundary

slug: classification

slug: ontology

slug: abstraction

slug: scale

slug: hierarchy

slug: compression

slug: invariance

slug: variability

slug: representation

slug: cardinality

slug: threshold

slug: feedback

slug: iteration

slug: approximation

components:
Equivalence Basis Criteria
Class Granularity Target
Split/Merge Decision Rule
Within-Class Homogeneity Test
Between-Class Separation Test
Invariant Preservation Check
Old-New Class Mapping
Downstream Effect Audit
Revision Versioning and Reversion Path
Hierarchical Resolution Ladder
Boundary Case Sample Set
Cost of Distinction Model
Fairness and Access Check
Multi-Stakeholder Resolution View
Empirical Cluster Signal

mechanisms: Classification Confusion or Error Matrix

essence_one_line: When current sameness classes are too coarse or too fine for the task, revise the equivalence relation with explicit split/merge rules, continuity mappings, and invariant checks.

compression_statement: Equivalence relations make systems tractable by deciding which cases count as the same for a purpose. Over time, those classes can become too broad, hiding meaningful differences, or too narrow, creating needless fragmentation. This archetype tunes the relation itself: define the basis for sameness, choose the needed class granularity, test homogeneity and separation, split or merge classes, map old classes to new ones, audit downstream consequences, and preserve invariants across the change.

core_intuition: A category system is not only a set of names; it is a working theory of what differences matter. Refinement says, “these things should no longer be treated as equivalent.” Coarsening says, “these distinctions no longer justify separate treatment.” Both operations are safe only when the criteria, mappings, and consequences are explicit.

canonical_formula: fit_for_purpose_equivalence = explicit_sameness_basis × granularity_target × split_merge_rule × invariant_preservation × continuity_mapping

structural_problem: A system relies on equivalence classes, but the current relation no longer matches the decisions it supports. If classes are too coarse, important differences are erased; if too fine, the system accumulates unnecessary complexity, duplicated treatment, and coordination burden. Changing the relation without explicit rules can break history, fairness, metrics, permissions, or interpretation.

trigger_conditions: Members inside one equivalence class show materially different behavior, risk, needs, outcomes, or treatment requirements.

trigger_conditions: Multiple classes receive the same action or interpretation and maintaining their separation adds cost without benefit.

trigger_conditions: Stakeholders disagree because they are using different implicit criteria for what counts as the same.

trigger_conditions: A classification, permission, taxonomy, or reporting system has grown beyond its original granularity assumptions.

trigger_conditions: Historical continuity matters because old and new class assignments must remain comparable or auditable.

trigger_conditions: Downstream decisions depend on class membership and would change if the equivalence relation were revised.

symptoms: A single class becomes a dumping ground for unlike cases that require many exceptions.

symptoms: Users create informal subcategories because the official relation is too coarse.

symptoms: Reports or dashboards contain many distinctions that lead to identical decisions.

symptoms: Class labels drift in meaning across teams, regions, model versions, or policy cycles.

symptoms: Schema or taxonomy changes orphan old records or make prior metrics incomparable.

symptoms: Boundary cases repeatedly trigger manual review because the equivalence basis is unclear.

root_tension: The system needs equivalence classes to simplify action, but simplification can either erase important differences or preserve distinctions that no longer matter. Relation change improves fit but threatens continuity and downstream stability.

anti_signatures: The task is merely to rename categories without changing membership or treatment.

anti_signatures: The system needs a fixed source of truth, not a change in the equivalence relation.

anti_signatures: The main issue is cross-schema translation between stable schemas rather than deciding whether classes should split or merge.

anti_signatures: The grouping is arbitrary or aesthetic and does not define sameness for a decision, representation, or obligation.

anti_signatures: The goal is one-way aggregation for reporting with no need to preserve membership criteria or old-new class mappings.

anti_signatures: The relation is intentionally stable because legal, scientific, or safety constraints prohibit ad hoc boundary changes.

structural_intervention: Convert implicit category drift into a governed equivalence-relation update: specify the sameness basis, set the target granularity, diagnose over-coarse and over-fine classes, decide splits and mergers by rule, preserve invariants, map old classes to new classes, update dependent systems, and review the result.

action_logic: Identify the decisions, representations, permissions, measurements, or obligations that depend on the equivalence relation.

action_logic: State the current equivalence basis: the properties or criteria that make two members count as the same for this purpose.

action_logic: Determine the target class granularity required by downstream action and interpretability.

action_logic: Test current classes for within-class heterogeneity and between-class redundancy.

action_logic: Split classes when hidden differences cause errors, unfairness, unsafe treatment, or repeated exceptions.

action_logic: Merge classes when maintained distinctions produce the same action, create noise, or overload users without adding value.

action_logic: Preserve declared invariants such as fairness, accounting totals, legal obligations, semantic continuity, or operational safety.

action_logic: Create an old-new mapping so historical records and downstream systems can translate across relation versions.

action_logic: Audit dependent processes and revise documentation, metrics, permissions, labels, or training materials.

action_logic: Monitor the revised relation for overfitting, fragmentation, erasure, and new boundary-case failures.

decision_rules: Refine when members currently treated as equivalent require reliably different actions or interpretations.

decision_rules: Coarsen when classes currently treated as different no longer justify different action, risk handling, cost, or representation.

decision_rules: Keep a distinction only when the benefit of preserving it exceeds the cognitive, administrative, computational, or fairness cost.

decision_rules: Do not change an equivalence relation without documenting the old-new mapping for affected records or obligations.

decision_rules: Require stronger review when class membership affects rights, access, identity, accountability, or safety.

decision_rules: Prefer hierarchical resolution when both coarse and fine class views are legitimately needed.

decision_rules: Treat empirical clusters as evidence for possible relation change, not as automatic authority to redefine equivalence.

target_invariants: The basis for equivalence remains explicit and reviewable.

target_invariants: Every split or merge has a reason tied to decision value, error reduction, fairness, continuity, or complexity management.

target_invariants: Old and new class assignments remain traceable across versions.

target_invariants: Downstream users can tell which class resolution they are using.

target_invariants: Important minority, edge, or high-risk cases are not erased by coarsening.

target_invariants: The revised relation is no more complex than the task can sustain.

target_invariants: Boundary cases receive a documented handling rule rather than silent exception drift.

expected_outcomes: Better fit between class structure and real decision needs.

expected_outcomes: Reduced false sameness when over-broad classes hide important distinctions.

expected_outcomes: Reduced needless fragmentation when over-fine distinctions do not change action.

expected_outcomes: More stable communication because stakeholders share explicit sameness criteria.

expected_outcomes: Improved historical traceability during taxonomy, schema, or policy updates.

expected_outcomes: Lower maintenance burden and fewer informal workaround categories.

expected_outcomes: Safer downstream changes because metrics, access, obligations, and records are audited.

prose_tradeoffs: Refinement improves precision but increases complexity. Coarsening improves tractability but risks erasure. Stable categories support continuity, but updated categories improve current fit. Empirical clustering can reveal structure, but it does not decide which distinctions matter normatively or operationally. Hierarchical systems preserve multiple resolutions, but they require disciplined mapping and maintenance.

Classification Boundary Refinement

short_definition: Split or adjust class boundaries when a current category hides meaningful heterogeneity or edge-case ambiguity.

distinctive_feature: The change is driven by insufficient within-class homogeneity.

distinct_from_parent: The parent covers both refinement and coarsening; this variant emphasizes class splitting or boundary sharpening.

still_same_parent_because: It still modifies the equivalence relation according to an explicit sameness criterion.

use_when: Members inside one class receive different outcomes or require different treatment.

use_when: Boundary cases repeatedly trigger disagreement or exception handling.

use_when: A diagnostic, eligibility, or reporting category is too broad for current decisions.

example_domains:
diagnostic categories
eligibility criteria
taxonomy maintenance

failure_modes: over-splitting categories until the system becomes unusable

failure_modes: sharpening boundaries without auditing downstream effects

Operational Class Coarsening

short_definition: Merge or aggregate classes when distinctions exist but are not useful enough for the operational decision at hand.

distinctive_feature: The change is driven by excessive granularity and insufficient between-class action difference.

distinct_from_parent: The parent includes bidirectional relation tuning; this variant focuses on merging and abstraction.

still_same_parent_because: The mechanism is still a controlled change in the equivalence relation.

use_when: Fine classes overload users, workflows, models, or reports.

use_when: Separate classes trigger the same action and can safely share treatment.

use_when: The system needs a higher-level view for coordination, triage, or governance.

example_domains:
executive dashboards
incident triage
statistical reporting

failure_modes: using coarsening to hide accountability or variance

name: Hierarchical Equivalence Resolution Ladder

short_definition: Maintain several nested equivalence resolutions so users can move between broad and detailed classes without losing mapping continuity.

distinctive_feature: The system preserves multiple valid resolutions rather than selecting only one.

distinct_from_parent: The parent can perform one update; this variant emphasizes a persistent multi-resolution class architecture.

still_same_parent_because: Each level is still defined by a relation among equivalent members and mappings between relations.

source_primes: equivalence_relation hierarchy scale

use_when: Different audiences need different class resolutions.

use_when: A single flat classification is either too coarse for specialists or too detailed for general use.

use_when: Reporting, operations, and analysis must share a compatible class structure.

example_domains:
product taxonomies
biological classification
organizational reporting

failure_modes: users treating different resolutions as incompatible taxonomies

Equivalence Crosswalk Preservation

short_definition: Treat the mapping between old and new equivalence classes as a first-class requirement during refinement or coarsening.

distinctive_feature: The preservation target is continuity across relation versions, not only improved current fit.

distinct_from_parent: The parent includes mapping as a component; this variant makes mapping the dominant design concern.

still_same_parent_because: It still operates by changing and documenting equivalence relations.

use_when: Historical records must remain interpretable after a class update.

use_when: Permissions, obligations, metrics, or classifications depend on old class membership.

use_when: A migration creates many-to-one, one-to-many, or many-to-many class relationships.

example_domains:
database migrations
policy category changes
statistical series revisions

failure_modes: orphaning records that do not map cleanly to new classes

near_names:
Relation Granularity Tuning
Class Split/Merge Design
Membership Boundary Refinement
Category Split or Merge Update