To thine own self be true: Psychological adjustment promotes judgeability via personality–behavior congruence.
Bibliographic record
Abstract
Well-adjusted individuals are highly judgeable in that their personalities tend to be seen more accurately than the personalities of less adjusted individuals (Colvin, 1993a, 1993b; Human & Biesanz, 2011a). The mechanisms behind this effect, however, are not well understood. How does adjustment facilitate judgeability? In the present video-perceptions study, we examined potential mechanisms through which adjustment could promote judgeability at 3 stages of the Realistic Accuracy Model (RAM; Funder, 1995): (a) cue relevance, (b) cue availability, and (c) cue detection. We found that well-adjusted individuals were more judgeable because they provided more relevant cues: Specifically, well-adjusted individuals behaved more in line with their distinctive personalities, which in turn led them to be seen more accurately. In contrast, neither cue availability nor detection could sufficiently account for the link between adjustment and judgeability. In sum, well-adjusted individuals are more judgeable because to their own selves, they are true.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".