Consensus Enables Accurate Social Judgments
Bibliographic record
Abstract
Ubiquitous to theories of social perception is an assumed relationship between an attribute’s (e.g., intelligence) “signal” and judgment accuracy, with accuracy impossible without the presence and consensual use of signal. Yet this foundational assumption remains untested. Our investigation focused on consensus (quantified using intraclass correlations, ICCs), which should suggest signal availability, according to theories of accurate social perception. Study 1 confirmed that judgments of different social attributes exhibit different degrees of consensus. Study 2 specifically tested the consensus → accuracy link, anticipating that social judgments with higher consensus (target ICCs) would show greater judgment accuracy. Using 497,780 judgments of 3,847 targets from 4,162 participants across 45 data sets testing a broad variety of social judgments, we found that consensus moderated the relationship between targets’ self-report and participants’ judgments: Judgment accuracy was higher when consensus was higher. Results show the first empirical support for a foundational assumption of theories of social perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".