Source Attribution

1. Hypothesis

A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve attribution accuracy and dispute resolution more than single-score, content-only or opaque automated approaches.

2. Experiment design

Design: end-to-end provenance assurance study across capture, editing, publication, redistribution and verification focused on Source Attribution Methods: content credential testing; cryptographic verification; metadata resilience analysis; media forensics; chain-of-custody audits; user comprehension testing; adversarial tampering; expert review; affected-user interviews; reproducibility testing; methods adapted specifically to Source Attribution Independent variables: evidence availability; provenance visibility; model or rule transparency; intervention timing; human-review level; platform context; user controls Dependent variables: attribution accuracy and dispute resolution; false-positive harm; user trust; correction or recovery time Confounders: anonymous speech and collaborative creation; platform differences; population composition; external events; baseline trust; data availability; reviewer expertise Measures: origin verification; metadata survival; tamper detection; attribution accuracy; disclosure comprehension; false-authenticity rate; verification latency; subtopic-specific indicators for attribution accuracy and dispute resolution; false-positive and false-negative rates; subgroup disparity; user comprehension; decision latency Success criteria: Statistically and practically meaningful improvement in the named outcomes; calibrated uncertainty; acceptable false-positive burden; no disproportionate subgroup harm; traceable evidence; contestable decisions; repeatable performance across relevant contexts. Failure conditions: No meaningful benefit; results depend on unavailable or intrusive data; false positives or chilling effects exceed benefit; performance fails under adversarial or cross-platform conditions; affected users cannot understand, contest or recover from decisions.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Source Attribution as a testable research proposition. Problem basis: Synthetic and transformed content can be distributed without reliable origin or edit history, while provenance mechanisms may be absent, removable, privacy-invasive or poorly understood. For Source Attribution, the specific challenge is assigning content or claims to the correct creator, institution or originating system. Directional expectation: If supported, the proposed approach should improve attribution accuracy and dispute resolution, reduce correction and recovery costs, and preserve legitimate expression, privacy, procedural fairness and user agency. Proposed observations: origin verification; metadata survival; tamper detection; attribution accuracy; disclosure comprehension; false-authenticity rate; verification latency; subtopic-specific indicators for attribution accuracy and dispute resolution; false-positive and false-negative rates; subgroup disparity; user comprehension; decision latency. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 33/100; Novelty 70/100; Strategic Importance 90/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. Source Attribution warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve attribution accuracy and dispute resolution more than single-score, content-only or opaque automated approaches.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Information integrity; platform governance; network science; computational social science; trust and safety; media forensics; human rights; behavioural science; literature specific to Source Attribution. It also records: C2PA — https://c2pa.org/; Content Authenticity Initiative — https://contentauthenticity.org/; W3C Verifiable Credentials — https://www.w3.org/TR/vc-data-model/; NIST media forensics resources — https://www.nist.gov/. Evidence register status: “Seeded; authoritative source register initiated; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Information integrity; platform governance; network science; computational social science; trust and safety; media forensics; human rights; behavioural science; literature specific to Source Attribution. References: C2PA — https://c2pa.org/; Content Authenticity Initiative — https://contentauthenticity.org/; W3C Verifiable Credentials — https://www.w3.org/TR/vc-data-model/; NIST media forensics resources — https://www.nist.gov/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.