Q002 — Self-Audit¶


Research	R0029 — Plural Voice Attribution
Run	2026-03-27
Query	Q002

ROBIS 4-Domain Audit¶

Domain 1: Eligibility Criteria¶

Rating: Low risk

Criterion	Assessment
Defined evidence requirements before searching	Yes — required quantitative survey data with disclosed methodology
Criteria consistent throughout	Yes — all sources evaluated against same threshold

Notes: Prioritized large-scale, methodologically transparent surveys over opinion pieces.

Domain 2: Search Comprehensiveness¶

Rating: Low risk

Criterion	Assessment
Multiple search strategies used	Yes — broad sentiment search plus targeted KPMG/Stanford searches
Searches designed to test each hypothesis	Yes — search terms neutral, not biased toward positive or negative
All results dispositioned	Yes — 30 total results, all accounted for
Source diversity achieved	Yes — three independent survey organizations across different methodologies

Notes: 4 searches (2 broad, 2 targeted), 30 total results, 5 selected, 25 rejected.

Domain 3: Evaluation Consistency¶

Rating: Low risk

Criterion	Assessment
All sources scored using same framework	Yes
Evidence typed consistently	Yes — all Statistical type
ACH matrix applied	Yes
Diagnosticity analysis performed	Yes

Notes: Consistent application. All evidence is statistical survey data, simplifying consistency.

Domain 4: Synthesis Fairness¶

Rating: Low risk

Criterion	Assessment
All hypotheses given fair hearing	Yes — H2 tested against evidence before elimination
Contradictory evidence surfaced	Yes — positive trend data (SRC03) noted even though H1 was partially supported
Confidence calibrated to evidence	Yes — High confidence justified by convergent large-scale surveys
Gaps acknowledged	Yes — content-type and tech-community gaps noted

Notes: The embedded assumption in the query ("negative attitudes") was surfaced and tested.

Overall Assessment¶

Overall risk of bias: Low risk

Strong evidence base with genuinely independent sources, large sample sizes, and convergent findings. The main limitation is that surveys measure attitudes toward "AI" generally rather than "AI-generated content" specifically.

Researcher Bias Check¶

Anchoring bias (low risk): The query's mention of "negative attitudes" could anchor analysis toward negativity. Mitigated by testing all three hypotheses equally and noting the positive trend data.
Western-centric bias (low risk): All three major surveys include global data, reducing the risk of overweighting Western attitudes.