Who makes a more consistent first impression? Examining the structure and correlates of dissensus
Bibliographic record
Abstract
OBJECTIVE AND BACKGROUND: How do targets shape consensus in impression formation? Targets are known to play an outsized role in the accuracy of first impressions, but their influence on consensus has been difficult to study. With the help of the recently developed extended Social Relations Model, we explore the structure and correlates of individual differences in consensus (i.e., dissensus). METHOD: Across 3 studies, 187 photographs of targets were rated by 960 perceivers on personality and evaluative traits, as well as being coded for physical cues by trained coders. We explored the within-target consistency of consensus across traits, as well as its relationship to four categories of theoretically relevant correlates: expressiveness, normativity, positivity, and social categories. RESULTS: The tendency to make a consistent impression on others was broadly consistent across traits. High-consensus targets tended to be more expressive, had more normative physical cues, and were viewed more positively. CONCLUSIONS: At least in a first impression context, targets may play a unique role in predicting the consensus of personality judgments by providing perceivers with more information to work with, and making a negative impression on others may carry social costs.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".