Knowledge Integrity

1. Hypothesis

A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve knowledge-graph consistency and update quality more than single-score, content-only or opaque automated approaches.

2. Experiment design

Design: claim-level information-quality evaluation programme with benchmark datasets, expert review and longitudinal correction tracking focused on Knowledge Integrity Methods: claim extraction; evidence retrieval; source-quality assessment; fact verification; calibration studies; knowledge-graph validation; correction propagation analysis; expert review; affected-user interviews; reproducibility testing; methods adapted specifically to Knowledge Integrity Independent variables: evidence availability; provenance visibility; model or rule transparency; intervention timing; human-review level; platform context; user controls Dependent variables: knowledge-graph consistency and update quality; false-positive harm; user trust; correction or recovery time Confounders: schema change and conflicting evidence; platform differences; population composition; external events; baseline trust; data availability; reviewer expertise Measures: claim accuracy; source quality; evidence sufficiency; confidence calibration; traceability; correction reach; retraction latency; residual misinformation; subtopic-specific indicators for knowledge-graph consistency and update quality; 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 Knowledge Integrity as a testable research proposition. Problem basis: Information systems optimise speed and reach more readily than evidence quality, allowing uncertain or false claims to accumulate authority through repetition, ranking and citation laundering. For Knowledge Integrity, the specific challenge is maintaining consistency, traceability and correction across connected bodies of knowledge. Directional expectation: If supported, the proposed approach should improve knowledge-graph consistency and update quality, reduce correction and recovery costs, and preserve legitimate expression, privacy, procedural fairness and user agency. Proposed observations: claim accuracy; source quality; evidence sufficiency; confidence calibration; traceability; correction reach; retraction latency; residual misinformation; subtopic-specific indicators for knowledge-graph consistency and update quality; 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. Knowledge Integrity 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 knowledge-graph consistency and update quality 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 Knowledge Integrity. It also records: International Fact-Checking Network — https://www.ifcncodeofprinciples.poynter.org/; Crossref — https://www.crossref.org/; Retraction Watch — https://retractionwatch.com/; UNESCO media and information literacy — https://www.unesco.org/. 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 Knowledge Integrity. References: International Fact-Checking Network — https://www.ifcncodeofprinciples.poynter.org/; Crossref — https://www.crossref.org/; Retraction Watch — https://retractionwatch.com/; UNESCO media and information literacy — https://www.unesco.org/

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.