Unethical behaviour in the military: The role of supervisor ethicality, ethical climate, and right-wing authoritarianism
Bibliographic record
Abstract
Using an anonymous self-report survey of 350 Canadian Armed Forces (CAF) personnel, this study investigated the effect of perceptions of the ethicality of one's immediate supervisor (supervisor ethics), right-wing authoritarianism (RWA), and ethical climate on self-reported unethical behavior in the form of discrimination and obeying an unlawful command (past behavior, behavioral intentions). As well, we investigated how supervisor ethics and RWA interact when predicting unethical behavior, and whether ethical climate mediated the relation between supervisor ethics and self-reported unethical behavior. Unethical behavior depended on perceptions of the ethicality of one's supervisor and RWA. RWA predicted discrimination toward a gay man (behavioral intentions), and supervisor ethics predicted discrimination against outgroups of people, and obedience of an unlawful command (past behavior). As well, the effects of ethical supervision on discrimination (past behavior, behavioral intentions) depended on participants' level of RWA . Finally, ethical climate mediated the relation between supervisor ethics and obeying an unlawful command, such that higher perceptions of supervisor ethics led to a higher ethical climate, which led to less obedience of an unlawful command in the past. This suggests that leaders can affect the ethical climate of on organization, which in turn affects ethical behavior of followers.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".