Individual differences in dissimilation: Do some people make more distinctions among targets' personalities than others?
Bibliographic record
Abstract
OBJECTIVE: People differ in how positively they tend to see others' traits, but people might also differ in how strongly they apply their perceptual styles. In two studies (Ns = 355, 303), the current research explores individual differences in how variable people's first impressions are across targets (i.e., within-person variability), how and why these differences emerge, and who varies more in their judgments of others. METHOD: Participants described themselves on personality measures and rated 30 (Study 1) or 90 (Study 2) targets on Big Five traits. RESULTS: Using the extended Social Relations Model (eSRM), results suggest that within-person variability in impressions is consistent across trait ratings. People lower in extraversion, narcissism and self-esteem tended to make distinctions across targets' Big Five traits that were more consistent with other perceivers (sensitivity). Furthermore, some people more than others tended to consistently make unique distinctions among targets (differentiation), and preliminary evidence suggests these people might be higher in social anxiety and lower in self-esteem and emotional stability. CONCLUSION: Overall then, a more complete account of person perception should consider individual differences in how variable people's impressions are of others.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".