Knowing versus liking: Separating normative knowledge from social desirability in first impressions of personality.
Bibliographic record
Abstract
There are strong differences between individuals in the tendency to view the personality of others as similar to the average person. That is, some people tend to form more normatively accurate impressions than do others. However, the process behind the formation of normatively accurate first impressions is not yet fully understood. Given that the average individual's personality is highly socially desirable (Borkenau & Zaltauskas, 2009; Wood, Gosling & Potter, 2007), individuals may achieve high normative accuracy by viewing others as similar to the average person or by viewing them in an overly socially desirable manner. The average self-reported personality profile and social desirability, despite being strongly correlated, independently and strongly predict first impressions. Further, some individuals have a more accurate understanding of the average individual's personality than do others. Perceivers with more accurate knowledge about the average individual's personality rated the personality of specific others more normatively accurately (more similar to the average person), suggesting that individual differences in normative judgments include a component of accurate knowledge regarding the average personality. In contrast, perceivers who explicitly evaluated others more positively formed more socially desirable impressions, but not more normatively accurate impressions.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".