Knowledge Provenance

1. Hypothesis

A transparent, participatory and human-directed approach to knowledge provenance, with explicit safeguards and longitudinal evaluation, will improve retrieval accuracy; provenance completeness; reuse; context retention; decay; decision quality; time-to-action compared with opaque, automation-first or short-term approaches.

2. Experiment design

Design: Prospective mixed-method study focused on Knowledge Provenance, combining controlled comparison, real-world implementation, subgroup analysis and longitudinal follow-up. Methods: knowledge graph analysis; provenance audit; retrieval testing; organisational ethnography; decay measurement; decision-use studies; literature and policy review; expert and affected-user interviews; reproducibility testing; methods adapted specifically to Knowledge Provenance Independent variables: intervention design; AI involvement; human control; duration; context; governance safeguards; participant characteristics; implementation fidelity Dependent variables: retrieval accuracy; provenance completeness; reuse; context retention; decay; decision quality; time-to-action; subtopic-specific outcomes for knowledge provenance; equity; unintended effects; recovery or adaptation time Confounders: age; culture; language; education; socioeconomic conditions; prior experience; baseline capability; institutional setting; technology access; external events Measures: retrieval accuracy; provenance completeness; reuse; context retention; decay; decision quality; time-to-action; validated subtopic measures; implementation fidelity; user-reported agency and burden; subgroup disparity; adverse and unexpected outcomes Success criteria: Statistically and practically meaningful benefit; preserved human agency and rights; acceptable burden; no disproportionate subgroup harm; transparent evidence; repeatable performance; viable implementation and recovery pathway. Failure conditions: No meaningful benefit; harms, dependency, exclusion or distortion exceed benefit; results fail outside narrow settings; affected people cannot understand or contest decisions; implementation or recovery is not viable.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Knowledge Provenance as a testable research proposition. Problem basis: Current systems address how knowledge is captured, connected, verified, reused and translated into action unevenly. For Knowledge Provenance, definitions, measures, safeguards and accountable implementation pathways remain fragmented or unvalidated. Directional expectation: If supported, the proposed approach should improve retrieval accuracy; provenance completeness; reuse; context retention; decay; decision quality; time-to-action; subtopic-specific outcomes for knowledge provenance; equity; unintended effects; recovery or adaptation time while reducing inequity, dependency, harm, coordination cost and recovery time. Proposed observations: retrieval accuracy; provenance completeness; reuse; context retention; decay; decision quality; time-to-action; validated subtopic measures; implementation fidelity; user-reported agency and burden; subgroup disparity; adverse and unexpected outcomes. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 34/100; Novelty 75/100; 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 Provenance 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, participatory and human-directed approach to knowledge provenance, with explicit safeguards and longitudinal evaluation, will improve retrieval accuracy; provenance completeness; reuse; context retention; decay; decision quality; time-to-action compared with opaque, automation-first or short-term 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: Interdisciplinary literature concerning how knowledge is captured, connected, verified, reused and translated into action; human-centred design; ethics; governance; systems research; literature specific to Knowledge Provenance. It also records: UNESCO Convention on Cultural Diversity — https://www.unesco.org/creativity/en/2005-convention; WIPO copyright resources — https://www.wipo.int/copyright/en/; UNESCO Recommendation on the Ethics of AI — https://www.unesco.org/en/artificial-intelligence/recommendation-ethics; OECD AI Principles — https://oecd.ai/; UN Declaration on the Rights of Indigenous Peoples — https://www.un.org/development/desa/indigenouspeoples/declaration-on-the-rights-of-indigenous-peoples.html. 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: Interdisciplinary literature concerning how knowledge is captured, connected, verified, reused and translated into action; human-centred design; ethics; governance; systems research; literature specific to Knowledge Provenance. References: UNESCO Convention on Cultural Diversity — https://www.unesco.org/creativity/en/2005-convention; WIPO copyright resources — https://www.wipo.int/copyright/en/; UNESCO Recommendation on the Ethics of AI — https://www.unesco.org/en/artificial-intelligence/recommendation-ethics; OECD AI Principles — https://oecd.ai/; UN Declaration on the Rights of Indigenous Peoples — https://www.un.org/development/desa/indigenouspeoples/declaration-on-the-rights-of-indigenous-peoples.html

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.