Examining the consistency of the good target across contexts and domains of personality
Bibliographic record
Abstract
Abstract Objective Good targets are those individuals who are seen more accurately than others. The present study examines the extent to which the good target is consistent across two domains and two contexts as well as how being perceived accurately is moderated by target well‐being. Method N = 194 participants completed a round‐robin forming first impressions design, wrote short essays on five life domains and completed a self‐report including measures of well‐being. An additional N = 211 participants read the essays to assess the author’s personality. We used the social accuracy model to allow for detailed analysis of individual differences among targets across traits and motives. Results We found support for the theory that the good target generalizes across both contexts and domains and also found evidence for a likable target. Target well‐being was not consistently associated with the good target across contexts and domains, though target well‐being was a consistent moderator for the likable target. Conclusion The good target is consistent across contexts and domains, but target well‐being is not a consistent moderator of distinctive accuracy beyond in‐person perceptions of traits. The likable target is more consistent across contexts and domains and has stronger links to target well‐being.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".