Seeing and Feeling Your Way to Accurate Personality Judgments
Bibliographic record
Abstract
Empathy, the practice of taking and emotionally identifying with another’s point of view, is a skill that likely provides context to another’s behavior. Yet systematic research on its relation with accurate personality trait judgment is sparse. This study investigated this relation between one’s empathic response tendencies (perspective taking, empathic concern, fantasy, and personal distress) and the accuracy with which she or he makes judgments of others. Using four different samples ( N = 1,153), the tendency to perspective take ( ds = .23–.27) and show empathic concern ( ds = .28–.42) were all positively related meta-analytically to distinctive accuracy, normative accuracy, and the assumed similarity of trait judgments. However, the empathic tendencies for fantasy and personal distress showed more complex patterns of relation. These findings are discussed in relation to previous literature, and in particular, why it is reasonable for empathy to be related to the accuracy of trait judgments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".