Assessing the moderating effects of ethical climate on the relation between social dominance orientation/right-wing authoritariansim and self-reported unethical behaviour
Bibliographic record
Abstract
Using an anonymous self-report survey of 364 Canadian Forces Army Anglophone personnel, this study investigated the effect that ethical climate has in moderating the relations between social dominance orientation (SDO) and right-wing authoritarianism (RWA) and self-reported unethical behaviour. Ethical climate as it relates to supervisor behaviour moderated the relation between RWA and self-reported discriminatory behaviour. The nature of the interaction was such that respondents who scored low in RWA and perceived a strong supervisor climate, reported fewer instances of past discriminatory behaviour, and less likelihood that they would discriminate in the future compared with three other groups: people who were low in RWA but perceived a weak supervisor climate, and people who were high in RWA and perceived a weak or strong supervisor climate. Ethical climate as it relates to rules moderated the relation between SDO and self-reported unethical behaviour. The nature of this interaction was such that people who scored low in SDO and perceived a strong rules climate reported fewer instances of unethical behaviour in the past, or less likelihood that they would engage in unethical behaviour in the future, compared with: people who were low in SDO but perceived a weak rules climate, and people who were high in SDO and perceived a weak or strong ethical climate as it pertains to rules. These results suggest that people who score higher versus lower in SDO and RWA tend to report more unethical behaviour regardless of the situational cues relating to ethical climate. Theoretical and practical implications of these results are 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".