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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".