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# Changelog
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All notable changes to this repository will be documented in this file.
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This project follows a draft-oriented versioning model while the specification remains experimental.
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---
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## [Unreleased]
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### Planned
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- Add additional v0.2 examples for:
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- high-confidence natural-source assessment
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- hybrid structural-originality assessment
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- disputed-origin assessment
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- declaration-conflict assessment
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- Add a dedicated review-routing model document.
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- Add relationship documents for:
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- CollapseMonitor
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- Royalty Readiness
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- Trace Protocol
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- Structure Fingerprint
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- Extend the schema to support:
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- detailed scoring model output
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- warning-flag detail objects
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- recursive synthetic risk output
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- review-routing metadata
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### Notes
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The next stage should focus on making the v0.2 model more operational without turning purity scoring into automatic legal or financial judgment.
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---
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## [0.2.0] - 2026-05-25
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### Added
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- Added initial `README.md` for **AI Purity Detection Algorithm v0.2**.
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- Added `docs/v0.2-roadmap.md` to define the proposed direction for the v0.2 specification.
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- Added `docs/scoring-weighting-model.md` to define a draft signal-weighting and confidence-adjusted scoring model.
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- Added `docs/warning-flag-severity-model.md` to define warning-flag severity levels, review routing, and downstream impact handling.
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- Added `schemas/purity-assessment.schema.json` for validating purity assessment examples.
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- Added `examples/purity-assessment.sample.yaml` as a standard purity assessment example.
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- Added `examples/purity-assessment.low-confidence.sample.yaml` to demonstrate weak provenance, unclear origin, low confidence, review-required routing, and blocked royalty-readiness status.
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- Added `examples/purity-assessment.recursive-synthetic-risk.sample.yaml` to demonstrate high recursive synthetic risk, weak primary-source provenance, high AI-pattern similarity, blocked training use, and blocked royalty-readiness status.
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- Added `.github/workflows/validate-examples.yml` to validate YAML examples against the JSON Schema.
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- Added `LICENSE`.
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### Defined
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- Defined `origin_purity_score` as a normalized estimate of source-origin composition.
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- Defined `ai_generated_ratio` as a separate estimate of AI-generated or AI-assisted content ratio.
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- Defined `warning_flags` as routing and review-support signals.
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- Defined a draft signal-weighting model using:
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- `provenance_evidence_score`
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- `author_declaration_score`
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- `structural_originality_score`
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- `revision_lineage_score`
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- `citation_transparency_score`
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- `ai_pattern_risk_score`
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- `structure_fingerprint_distinctiveness_score`
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- `recursive_synthetic_risk_score`
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- `signal_confidence_quality_score`
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- Defined warning severity levels:
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- `info`
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- `warning`
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- `review_required`
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- `blocking`
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- Defined review-routing modes:
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- `none`
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- `recommended`
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- `required`
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- `blocking_review`
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### Validation
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- Added GitHub Actions validation for:
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- `examples/purity-assessment.sample.yaml`
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- `examples/purity-assessment.low-confidence.sample.yaml`
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- `examples/purity-assessment.recursive-synthetic-risk.sample.yaml`
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- Confirmed that all three example files pass validation against:
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- `schemas/purity-assessment.schema.json`
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### Notes
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This release establishes **AI Purity Detection Algorithm v0.2** as a draft algorithmic layer for source-preservation systems.
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The specification focuses on:
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- origin-purity scoring
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- natural / synthetic data separation
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- hybrid data classification
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- recursive synthetic risk detection
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- warning-flag severity
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- confidence handling
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- review routing
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- downstream-use guidance
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- model-collapse monitoring support
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- royalty-readiness review support
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This release does not attempt to:
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- prove legal authorship
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- determine copyright ownership
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- automatically assign royalties
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- ban AI-assisted creation
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- punish synthetic content
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- perfectly detect AI-generated text
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- replace human or multi-wing review
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The core principle remains:
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```text
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Purity is not moral judgment.
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Purity is ecological signal.

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