Volitional Trait Change in Selection: It's About Time, but Also Degree and Perspective
Bibliographic record
Abstract
ABSTRACT Volitional trait change—the idea that people can willfully change their personality—marks an important advancement for personality science beyond historic views of traits as relatively immutable. We applaud Dupré and Wille's (2024) extension and application of how volitional trait change could impact personnel selection. In this response, we aim to contribute to this discussion by focusing on three critical considerations for the applicability of PDGs to personnel selection—time (how quickly traits can change), degree (how much traits can change), and perspective (who perceives trait change). We concur that personality development has untapped potential in personnel selection and offer suggestions and caveats for how organizations might best realize it. We are excited about a new frontier in extending personality development to organizational settings and are optimistic that doing so would appreciably benefit employees, organizations, and the selection literature.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".