Personality Correlates of Workplace Anti‐Social Behavior
Bibliographic record
Abstract
La relation entre le comportement antisocial au travail (ASB) et des traits de personnalité généraux a étéétudiée sur un échantillon composé de 267 salariés coréens. L’ASB au travail a été conceptualiséà partir de deux dimensions, l’ASB dirigée contre les individues (ASBI) et l’ASB dirigée contre l’organisation (ASBO). La personnalité a étéévaluée grâce au Big Five et à un sixième facteur ≪honnêteté‐humilité≫ (). Les analyses de régression multiple indiquèrent que l’honnêteté‐humilité et l’extraversion jouaient un rôle essentiel dans la prédiction de l’ASBI et de l’ASBO. De plus, Agreeableness était reliée à l’ASBI tandis que Conscientiousness l’était à l’ASBO. On parle aussi de la nécessité d’adopter un modèle optimal des traits de personnalité. The relationship between workplace Anti‐Social Behavior (ASB) and higher‐order personality traits was investigated using a sample of 267 Korean employees. Workplace ASB was conceptualized as having two dimensions: ASB directed against individuals (ASBI) and ASB directed against the organization (ASBO). Personality was assessed using adjective measures of the Big Five factors and of a sixth factor, Honesty‐Humility (). Multiple regression analyses indicated that Honesty‐Humility and Extraversion played prominent roles in predicting both ASBI and ASBO. Also, Agreeableness was related to ASBI, whereas Conscientiousness was related to ASBO. The importance of adopting an optimal framework of personality traits was discussed.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".