Collective Intelligence

1. Hypothesis

A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve decision accuracy, diversity use and error correction more than single-score, content-only or opaque automated approaches.

2. Experiment design

Design: participatory longitudinal community-health study with subgroup, network, discourse and intervention analysis focused on Collective Intelligence Methods: community surveys; network and discourse analysis; participatory research; moderation experiments; deliberative trials; longitudinal cohort tracking; incident review; expert review; affected-user interviews; reproducibility testing; methods adapted specifically to Collective Intelligence Independent variables: evidence availability; provenance visibility; model or rule transparency; intervention timing; human-review level; platform context; user controls Dependent variables: decision accuracy, diversity use and error correction; false-positive harm; user trust; correction or recovery time Confounders: expertise distribution and coordination cost; platform differences; population composition; external events; baseline trust; data availability; reviewer expertise Measures: belonging; participation diversity; cohesion; polarisation; toxicity; harassment exposure; collective intelligence; recovery after disruption; subtopic-specific indicators for decision accuracy, diversity use and error correction; 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 Collective Intelligence as a testable research proposition. Problem basis: Community health is often inferred from activity or growth metrics that can conceal exclusion, polarisation, harassment, domination by a few actors and declining capacity for collective problem-solving. For Collective Intelligence, the specific challenge is testing conditions under which groups combine diverse knowledge to outperform individuals or homogeneous groups. Directional expectation: If supported, the proposed approach should improve decision accuracy, diversity use and error correction, reduce correction and recovery costs, and preserve legitimate expression, privacy, procedural fairness and user agency. Proposed observations: belonging; participation diversity; cohesion; polarisation; toxicity; harassment exposure; collective intelligence; recovery after disruption; subtopic-specific indicators for decision accuracy, diversity use and error correction; 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. Collective Intelligence 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 decision accuracy, diversity use and error correction 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 Collective Intelligence. It also records: OECD trust and civic participation work — https://www.oecd.org/; UNESCO social and human sciences — https://www.unesco.org/; Council of Europe digital citizenship resources — https://www.coe.int/; Australian Human Rights Commission — https://humanrights.gov.au/. 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 Collective Intelligence. References: OECD trust and civic participation work — https://www.oecd.org/; UNESCO social and human sciences — https://www.unesco.org/; Council of Europe digital citizenship resources — https://www.coe.int/; Australian Human Rights Commission — https://humanrights.gov.au/

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.