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Contribution Tolerance Band Model\n\nContribution Tolerance Band Model defines contribution not as a single exact value, but as an interval-based estimate with an explainable tolerance band.\n\nIt is designed for royalty allocation, trace evaluation, attribution systems, and dispute-resistant value distribution under imperfect evidence.\n\n---\n\n## One-Line Definition\n\nContribution Tolerance Band Model v0.1 is an uncertainty-aware contribution assessment model that represents contribution as a range rather than a fixed point.\n\nInstead of asking:\n\n\u003e “Who contributed exactly 47%?”\n\nthis model asks:\n\n\u003e “Within what explainable range can this contribution be fairly assessed?”\n\n---\n\n## Why This Exists\n\nIn AI-mediated creation, protocol design, writing, software development, research, and trace-based royalty systems, contribution is often difficult to measure precisely.\n\nA contribution may appear as:\n\n- an original question\n- a structural idea\n- a protocol design\n- an implementation\n- an edit\n- a transformation\n- an indirect influence\n- a long-term trace\n- a disputed or partially visible lineage\n\nBecause of this, exact contribution scoring can create false precision.\n\nFor example:\n\n```text\nContributor A: 47%\nContributor B: 38%\nContributor C: 15%\n```\n\nThis looks precise, but the evidence may not justify that level of certainty.\n\nContribution Tolerance Band Model avoids false precision by using tolerance bands.\n\n```text\nContribution Range = estimated_value ± tolerance_band\n```\n\nExample:\n\n```text\nContributor A: 42% - 50%\nContributor B: 35% - 43%\nContributor C: 12% - 18%\n```\n\nThe purpose is not to eliminate uncertainty.\n\nThe purpose is to make uncertainty visible, bounded, explainable, and governable.\n\n---\n\n## Core Concept\n\nContribution should not be treated as a fixed point.\n\nIt should be treated as a flexible but auditable range.\n\n```text\nC = μ ± ε\n```\n\nWhere:\n\n- `C` = contribution range\n- `μ` = estimated contribution value\n- `ε` = tolerance band\n- `μ - ε` = lower bound\n- `μ + ε` = upper bound\n\nExample:\n\n```json\n{\n  \"contributor_id\": \"contributor_A\",\n  \"estimated_value\": 0.46,\n  \"tolerance_band\": 0.04,\n  \"range\": {\n    \"lower_bound\": 0.42,\n    \"upper_bound\": 0.50\n  }\n}\n```\n\nA narrow band means stronger evidence and higher confidence.\n\nA wide band means greater uncertainty and may trigger review, pooled allocation, deferred allocation, or dispute handling.\n\n---\n\n## Core Principle\n\nDo not force exactness where exactness cannot be justified.\n\nThis model does not claim to calculate the absolute truth of contribution.\n\nInstead, it provides a structured way to estimate contribution under uncertainty.\n\nThe central principle is:\n\n\u003e Make uncertainty visible, bounded, explainable, and governable.\n\n---\n\n## Good Uncertainty vs Bad Ambiguity\n\nThis model distinguishes between controlled uncertainty and uncontrolled ambiguity.\n\n### Good Uncertainty\n\nGood uncertainty is explainable.\n\nIt may come from:\n\n- incomplete but meaningful evidence\n- partial structural similarity\n- moderate trace confidence\n- estimator disagreement within acceptable limits\n- indirect but plausible influence\n\nExample:\n\n```text\nContributor A: 42% - 50%\n```\n\nThis is acceptable if the band width is justified.\n\n### Bad Ambiguity\n\nBad ambiguity is too broad to support safe allocation.\n\nExample:\n\n```text\nContributor A: 10% - 90%\n```\n\nThis should not be used for automatic allocation.\n\nIt should trigger review, dispute handling, deferred allocation, or shared pool handling.\n\n---\n\n## Model Structure\n\nA Contribution Tolerance Band assessment may include:\n\n- contribution assessment metadata\n- target work information\n- contributor assessments\n- estimated values\n- tolerance bands\n- contribution ranges\n- evidence bundles\n- estimator outputs\n- uncertainty factors\n- contribution layers\n- allocation recommendations\n- review triggers\n- governance metadata\n- relationships to other trace or royalty systems\n\n---\n\n## Contribution Layers\n\nFor human readability, contribution may also be expressed as layers.\n\nInstead of relying only on numbers, the model can classify contributors as:\n\n```text\nPrimary Contribution Layer\nNear Contribution Layer\nSupporting Contribution Layer\nShared Influence Layer\nUnresolved / Disputed Layer\n```\n\nThis reduces unnecessary conflict caused by excessive numerical precision.\n\nExample:\n\n```json\n{\n  \"primary_contribution_layer\": [\"contributor_A\"],\n  \"near_contribution_layer\": [\"contributor_B\"],\n  \"supporting_contribution_layer\": [\"contributor_C\"],\n  \"shared_influence_layer\": [\"contributor_D\"],\n  \"unresolved_layer\": []\n}\n```\n\n---\n\n## Allocation Behavior\n\nContribution Tolerance Band Model does not directly enforce payment.\n\nIt provides allocation guidance.\n\nPossible allocation outcomes include:\n\n```text\nautomatic_allocation\npooled_allocation\nequalized_allocation\nhuman_review\ndispute_registry\ndeferred_allocation\ncommon_culture_pool\nno_allocation\n```\n\n### Automatic Allocation\n\nUsed when:\n\n- evidence is strong\n- tolerance bands are narrow\n- estimator agreement is high\n- dispute risk is low\n\n### Pooled Allocation\n\nUsed when:\n\n- contributor ranges overlap\n- contributions are difficult to separate\n- influence is shared\n- strict ranking would create unnecessary conflict\n\n### Human Review\n\nUsed when:\n\n- tolerance bands are too wide\n- evidence is conflicting\n- estimator agreement is low\n- high-value distribution is involved\n\n### Common Culture Pool\n\nUsed when:\n\n- influence may be real but not individually attributable\n- origin is unclear\n- contribution has become broadly cultural\n- evidence is too weak for individual allocation\n\n---\n\n## Yin-Yang Balance Interpretation\n\nThis model may be interpreted as a Yin-Yang balance system.\n\n### Yang Evidence\n\nYang represents visible, explicit, externalized evidence.\n\nExamples:\n\n- citation\n- signature\n- timestamp\n- code commit\n- publication history\n- explicit reference\n- repository history\n- C2PA-style provenance\n- signed attestation\n\n### Yin Influence\n\nYin represents hidden, structural, indirect, or long-term influence.\n\nExamples:\n\n- conceptual resonance\n- structural similarity\n- origin-question influence\n- implicit inspiration\n- design lineage\n- cultural diffusion\n- long-term transformation\n\nA healthy contribution model should not rely only on Yang evidence.\n\nIt should also recognize Yin influence.\n\nHowever, it should not rely only on Yin influence either.\n\nUnverified influence claims must not automatically generate strong allocation rights.\n\nTherefore, this model seeks a middle balance.\n\n```text\nToo much Yang  → only explicit records matter\nToo much Yin   → vague influence claims dominate\nMiddle Balance → evidence and structure are both considered\n```\n\nThis is the willow model of allocation:\n\n\u003e flexible enough to bend with uncertainty,  \n\u003e rooted enough to remain auditable.\n\n---\n\n## Repository Structure\n\n```text\ncontribution-tolerance-band-model/\n├─ .github/\n│  └─ workflows/\n│     └─ validate-specs.yml\n├─ docs/\n│  ├─ contribution-tolerance-band-model.md\n│  ├─ relationship-to-royalty-os.md\n│  ├─ relationship-to-ksd.md\n│  ├─ relationship-to-trace-protocol.md\n│  ├─ relationship-to-dispute-registry.md\n│  ├─ allocation-readiness.md\n│  ├─ review-status-notes.md\n│  ├─ governance-bridge-notes.md\n│  ├─ band-width-function.md\n│  ├─ model-weighting-notes.md\n│  ├─ temporal-adjustment-notes.md\n│  ├─ anti-gaming-rules.md\n│  └─ lineage-engine-integration.md\n├─ examples/\n│  └─ contribution-tolerance-band.sample.json\n├─ schemas/\n│  └─ contribution-tolerance-band-v0.1.schema.json\n├─ LICENSE\n└─ README.md\n```\n\n---\n\n## Start Here\n\n- [`docs/contribution-tolerance-band-model.md`](docs/contribution-tolerance-band-model.md)  \n  Main explanatory document for the Contribution Tolerance Band Model.\n\n- [`schemas/contribution-tolerance-band-v0.1.schema.json`](schemas/contribution-tolerance-band-v0.1.schema.json)  \n  JSON Schema for validating Contribution Tolerance Band records.\n\n- [`examples/contribution-tolerance-band.sample.json`](examples/contribution-tolerance-band.sample.json)  \n  Example record showing interval-based contribution assessment.\n\n- [`.github/workflows/validate-specs.yml`](.github/workflows/validate-specs.yml)  \n  GitHub Actions workflow for validating schema compliance and numerical consistency.\n\n---\n\n## Related Documents\n\n### Core Model\n\n- [`docs/contribution-tolerance-band-model.md`](docs/contribution-tolerance-band-model.md)  \n  Main explanatory document for the Contribution Tolerance Band Model.\n\n- [`docs/band-width-function.md`](docs/band-width-function.md)  \n  Defines how tolerance band width should widen or narrow based on evidence, trace confidence, estimator variance, and dispute risk.\n\n- [`docs/model-weighting-notes.md`](docs/model-weighting-notes.md)  \n  Explains how multiple estimators should be weighted, aggregated, adjusted, and audited.\n\n- [`docs/temporal-adjustment-notes.md`](docs/temporal-adjustment-notes.md)  \n  Explains how contribution ranges, confidence, evidence strength, and allocation readiness may change over time.\n\n### Governance and Allocation\n\n- [`docs/allocation-readiness.md`](docs/allocation-readiness.md)  \n  Defines when a contribution assessment is stable enough to influence allocation.\n\n- [`docs/review-status-notes.md`](docs/review-status-notes.md)  \n  Explains assessment lifecycle states, review reasons, and review transitions.\n\n- [`docs/governance-bridge-notes.md`](docs/governance-bridge-notes.md)  \n  Connects assessment, review, readiness, dispute handling, and Royalty OS allocation policy.\n\n- [`docs/anti-gaming-rules.md`](docs/anti-gaming-rules.md)  \n  Defines safeguards against artificial ambiguity, trace spam, overclaiming, false provenance, bad-faith disputes, and other manipulation risks.\n\n### Relationship Documents\n\n- [`docs/relationship-to-royalty-os.md`](docs/relationship-to-royalty-os.md)  \n  Explains how this model serves as the uncertainty-aware contribution layer for Royalty OS.\n\n- [`docs/relationship-to-ksd.md`](docs/relationship-to-ksd.md)  \n  Explains how KSD and Structure Fingerprint provide structural evidence for contribution assessment.\n\n- [`docs/relationship-to-trace-protocol.md`](docs/relationship-to-trace-protocol.md)  \n  Explains how Trace Protocol records become trace evidence for contribution ranges.\n\n- [`docs/relationship-to-dispute-registry.md`](docs/relationship-to-dispute-registry.md)  \n  Explains how disputed or contested contribution assessments should be governed.\n\n- [`docs/lineage-engine-integration.md`](docs/lineage-engine-integration.md)  \n  Explains how lineage records connect to contribution ranges, allocation readiness, dispute handling, and Royalty OS policy.\n\n---\n\n## Schema Usage\n\nThis repository uses JSON Schema Draft 2020-12.\n\nThe main schema is located at:\n\n```text\nschemas/contribution-tolerance-band-v0.1.schema.json\n```\n\nThe example file is located at:\n\n```text\nexamples/contribution-tolerance-band.sample.json\n```\n\nThe schema validates the structure of a Contribution Tolerance Band assessment, including:\n\n- required metadata\n- contributor records\n- contribution ranges\n- evidence bundles\n- estimator outputs\n- allocation recommendations\n- governance metadata\n- relationship records\n\n---\n\n## Validation\n\nThis repository includes a GitHub Actions workflow:\n\n```text\n.github/workflows/validate-specs.yml\n```\n\nThe workflow performs both JSON Schema validation and additional custom consistency checks.\n\n### JSON Schema Validation\n\nThe workflow validates:\n\n- schema compliance\n- required fields\n- allowed enum values\n- object structure\n- value ranges\n- URI and date-time formats\n\n### Custom Numerical Validation\n\nThe workflow also checks numerical consistency that JSON Schema alone cannot fully enforce.\n\nIt validates:\n\n```text\nlower_bound \u003c= upper_bound\nlower_bound = estimated_value - tolerance_band\nupper_bound = estimated_value + tolerance_band\nallocation_share total = 1.0\nshared_pool_policy.pool_share consistency\nreview_required / review_reasons consistency\n```\n\nThis prevents invalid contribution ranges from passing validation.\n\n---\n\n## Example\n\nMinimal contributor range example:\n\n```json\n{\n  \"contributor_id\": \"contributor_A\",\n  \"estimated_value\": 0.46,\n  \"tolerance_band\": 0.04,\n  \"range\": {\n    \"lower_bound\": 0.42,\n    \"upper_bound\": 0.50\n  },\n  \"confidence\": 0.82,\n  \"evidence_strength\": 0.78,\n  \"trace_confidence\": 0.74,\n  \"estimator_agreement\": 0.81,\n  \"dispute_risk\": 0.12,\n  \"contribution_layer\": \"primary\"\n}\n```\n\nThis means the contributor is not treated as having exactly `46%`.\n\nInstead, the contributor is assessed within an explainable range:\n\n```text\n42% - 50%\n```\n\n---\n\n## Review Triggers\n\nA contribution assessment may require review when:\n\n```text\ntolerance_band is too wide\nevidence_strength is too low\ntrace_confidence is too low\nestimator_agreement is too low\ndispute_risk is too high\nranges overlap in a conflict-sensitive way\ncounter-evidence exists\nhigh-value distribution is involved\nmanual review is requested\n```\n\nWhen review is required, allocation may be:\n\n- deferred\n- pooled\n- sent to human review\n- sent to dispute handling\n- placed into a shared influence pool\n- placed into a common culture pool\n\n---\n\n## Band Width Function\n\nBand Width Function defines how the `tolerance_band` should be calculated or justified.\n\nThe band should widen or narrow based on:\n\n- evidence strength\n- structure ambiguity\n- trace uncertainty\n- estimator variance\n- dispute risk\n- temporal uncertainty\n- manipulation risk\n\nCore idea:\n\n```text\nstrong evidence → narrower band\nweak evidence   → wider band\n```\n\nSee also: [`docs/band-width-function.md`](docs/band-width-function.md)\n\n---\n\n## Model Weighting\n\nModel Weighting explains how multiple estimators should be combined.\n\nEstimators may include:\n\n- structure estimator\n- provenance estimator\n- trace estimator\n- similarity estimator\n- human reviewer\n- community review\n- dispute-aware estimator\n- domain expert\n\nThe goal is to avoid dependence on a single estimator.\n\nSee also: [`docs/model-weighting-notes.md`](docs/model-weighting-notes.md)\n\n---\n\n## Temporal Adjustment\n\nTemporal Adjustment explains how contribution assessments may change over time.\n\nTime may:\n\n- strengthen evidence\n- weaken trace confidence\n- clarify lineage\n- increase cultural diffusion\n- reveal counter-evidence\n- resolve disputes\n- require supersession\n\nCore idea:\n\n\u003e Time should not erase contribution, but time may change how contribution is interpreted.\n\nSee also: [`docs/temporal-adjustment-notes.md`](docs/temporal-adjustment-notes.md)\n\n---\n\n## Anti-Gaming Rules\n\nAnti-Gaming Rules protect the model from manipulation.\n\nThey address risks such as:\n\n- artificial ambiguity\n- range inflation\n- trace spam\n- false provenance\n- structure overclaim\n- similarity overclaim\n- bad-faith disputes\n- retroactive claim insertion\n- evidence flooding\n- model weighting manipulation\n- temporal gaming\n\nCore rule:\n\n\u003e Uncertainty should not become entitlement.\n\nSee also: [`docs/anti-gaming-rules.md`](docs/anti-gaming-rules.md)\n\n---\n\n## Relationship to Royalty OS\n\nContribution Tolerance Band Model can serve as an uncertainty-aware contribution layer for Royalty OS and trace-based allocation systems.\n\nRoyalty OS may use this model to determine whether a contribution case should result in:\n\n- automatic allocation\n- pooled allocation\n- equalized allocation\n- human review\n- dispute registry handling\n- deferred allocation\n- common culture pool allocation\n\nThis model does not replace Royalty OS.\n\nIt strengthens Royalty OS by preventing false precision in contribution scoring.\n\nSee also: [`docs/relationship-to-royalty-os.md`](docs/relationship-to-royalty-os.md)\n\n---\n\n## Relationship to KSD / Structure Fingerprint\n\nKSD and Structure Fingerprint provide structural evidence.\n\nThey help determine:\n\n- whether two works share structural lineage\n- whether a question, pattern, or architecture has been inherited\n- whether similarity is superficial or structural\n- whether indirect influence may exist\n\nContribution Tolerance Band Model uses this evidence to adjust the tolerance band.\n\nStrong structural evidence narrows uncertainty.\n\nWeak or ambiguous structural evidence widens uncertainty.\n\nSee also: [`docs/relationship-to-ksd.md`](docs/relationship-to-ksd.md)\n\n---\n\n## Relationship to Trace Protocol\n\nTrace Protocol provides trace paths.\n\nThese may include:\n\n- access trace\n- influence trace\n- audit trace\n- reference trace\n- transformation trace\n\nContribution Tolerance Band Model uses trace confidence to determine whether the contribution range should be narrow or wide.\n\nClear trace paths generally narrow the tolerance band.\n\nMissing, indirect, or disputed trace paths generally widen the tolerance band.\n\nSee also: [`docs/relationship-to-trace-protocol.md`](docs/relationship-to-trace-protocol.md)\n\n---\n\n## Relationship to Lineage Engine\n\nLineage Engine identifies possible origin, derivation, adaptation, influence, or structural inheritance.\n\nContribution Tolerance Band Model converts those lineage signals into uncertainty-aware contribution ranges.\n\nLineage may include:\n\n- origin candidate\n- derived from\n- adapted from\n- influenced by\n- translated from\n- remixed from\n- implemented from\n- extended from\n- structurally similar to\n- conceptually related to\n\nLineage is evidence of relationship.\n\nIt is not automatic proof of contribution share.\n\nSee also: [`docs/lineage-engine-integration.md`](docs/lineage-engine-integration.md)\n\n---\n\n## Relationship to Signed Impact Attestation\n\nSigned Impact Attestation provides accountable claims.\n\nIf an evaluator, institution, or reviewer claims that a contributor had a certain impact, that claim can be included as evidence.\n\nHowever, a signed claim does not automatically determine final contribution.\n\nIt may affect:\n\n- evidence strength\n- dispute risk\n- estimator confidence\n- review status\n\n---\n\n## Relationship to Dispute Registry\n\nIf a contribution estimate is challenged, the case may be sent to Dispute Registry.\n\nDispute Registry may record:\n\n- objection\n- correction\n- supersession\n- revocation\n- reaffirmation\n- review outcome\n\nContribution Tolerance Band Model should not hide disputes.\n\nIt should make uncertain or contested contribution visible.\n\nSee also: [`docs/relationship-to-dispute-registry.md`](docs/relationship-to-dispute-registry.md)\n\n---\n\n## Allocation Readiness\n\nAllocation Readiness defines when a Contribution Tolerance Band assessment is sufficiently stable to influence allocation.\n\nA contribution assessment should not automatically become an allocation decision.\n\nIt must pass a readiness gate based on:\n\n- tolerance band width\n- evidence strength\n- trace confidence\n- estimator agreement\n- dispute risk\n- review status\n- value at risk\n\nSee also: [`docs/allocation-readiness.md`](docs/allocation-readiness.md)\n\n---\n\n## Review Status\n\nReview Status defines how contribution assessments express lifecycle state and review requirements.\n\nAn assessment may be:\n\n```text\ndraft\nactive\nreview_required\ndisputed\nsuperseded\nrevoked\narchived\n```\n\nReview status prevents unstable assessments from being mistaken for final decisions.\n\nSee also: [`docs/review-status-notes.md`](docs/review-status-notes.md)\n\n---\n\n## Governance Bridge\n\nGovernance Bridge connects assessment, review, allocation readiness, dispute handling, and Royalty OS allocation policy.\n\nIts core rule is:\n\n\u003e A contribution range should never become an allocation decision without governance.\n\nSee also: [`docs/governance-bridge-notes.md`](docs/governance-bridge-notes.md)\n\n---\n\n## Non-Goals\n\nThis model does not:\n\n- determine legal ownership\n- replace copyright law\n- prove absolute originality\n- guarantee perfect fairness\n- calculate the metaphysical truth of influence\n- enforce payment by itself\n- eliminate all disputes\n- replace human judgment in high-risk cases\n\nThis model provides an uncertainty-aware structure for fairer contribution assessment.\n\n---\n\n## Design Philosophy\n\nContribution is not always a fixed number.\n\nIn creative, technical, and AI-mediated environments, contribution often behaves like a flexible structure.\n\nIt bends with evidence.  \nIt shifts with new traces.  \nIt narrows with stronger proof.  \nIt widens under uncertainty.  \nIt may require review when conflict appears.\n\nTherefore, a mature royalty or attribution system should not pretend to have perfect certainty.\n\nIt should preserve flexibility without losing accountability.\n\nThe system must be like a willow:\n\n\u003e flexible enough to bend with uncertainty,  \n\u003e rooted enough to remain auditable.\n\n---\n\n## Current Status\n\n```text\nVersion: v0.1\nStatus: Draft\nSchema: JSON Schema Draft 2020-12\nValidation: GitHub Actions\n```\n\nThis repository currently provides:\n\n```text\nschemas/\n  contribution-tolerance-band-v0.1.schema.json\n\nexamples/\n  contribution-tolerance-band.sample.json\n\ndocs/\n  contribution-tolerance-band-model.md\n  relationship-to-royalty-os.md\n  relationship-to-ksd.md\n  relationship-to-trace-protocol.md\n  relationship-to-dispute-registry.md\n  allocation-readiness.md\n  review-status-notes.md\n  governance-bridge-notes.md\n  band-width-function.md\n  model-weighting-notes.md\n  temporal-adjustment-notes.md\n  anti-gaming-rules.md\n  lineage-engine-integration.md\n\n.github/workflows/\n  validate-specs.yml\n```\n\n---\n\n## License\n\nSee [`LICENSE`](LICENSE).\n\n---\n\n## Summary\n\nContribution Tolerance Band Model v0.1 introduces:\n\n- interval-based contribution estimation\n- explainable tolerance bands\n- evidence-aware uncertainty\n- estimator consensus\n- model weighting\n- temporal adjustment\n- anti-gaming safeguards\n- lineage integration\n- range overlap handling\n- review triggers\n- allocation readiness\n- shared pool handling\n- dispute-aware allocation guidance\n- governance-aware review flow\n- machine-readable validation\n\nIts central principle is:\n\n\u003e Do not force exactness where exactness cannot be justified.  \n\u003e Make uncertainty visible, bounded, explainable, and governable.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamuraiwriter7%2Fcontribution-tolerance-band-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamuraiwriter7%2Fcontribution-tolerance-band-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamuraiwriter7%2Fcontribution-tolerance-band-model/lists"}