Little Between-Region and Between-Country Variance When People Form Impressions of Others
Bibliographic record
Abstract
To what extent are perceivers' first impressions of other individuals dictated by cultural background rather than personal idiosyncrasies? To address this question, we analyzed a globally diverse data set containing 11,481 adult participants' ratings of 120 targets across 45 countries (2,597,624 total ratings). Across ratings of 13 traits, we found that perceivers' idiosyncratic differences accounted for approximately 29% of variance and impressions on their own and approximately 16% in conjunction with target characteristics. However, country- and region-level differences, here a proxy for culture, accounted for 3.2% on average (i.e., both alone and in conjunction with target characteristics). We replicated this pattern of effects in a preregistered analysis on an entirely novel data set containing 7,007 participants' ratings of 100 targets across 41 countries (24,886 total ratings). Together, these results suggest that perceivers' impressions of other people are largely dictated by their individual characteristics and local environment rather than their cultural background.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".