Bibliographic record
Abstract
Morality is often described as a moral matrix—a consensual hallucination—in which it is difficult to “step outside” and understand different moral matrices. A new synthesis in the study of moral psychology has converged on a set of psychological foundations on which cultures build moral matrices. Individuals from western, educated, industrialized, rich and democratic (WEIRD) societies primarily rely on individualizing foundations (concerns for harm & fairness) while most other societies have a boarder moral matrix that also includes binding foundations (e.g., concerns for authority & in-group loyalty; See Haidt, 2007). Given that the study of “moral disengagement” in sport has mainly examined WEIRD societies (e.g., Canada, United States) the purpose of this study was to test if the moral disengagement in sport scale (short) actually measures, in part, an orientation towards binding foundations. Study one examined current and former athletes (n = 171) from western countries (e.g., Canada, United States). Bias corrected bootstrap analyses revealed that moral disengagement not only mediated the negative relationship between individualizing foundations and common moral dilemmas in sport (-.33, 95% CI = -.46 - -.22), but also for the positive association between binding foundations and moral dilemmas (.17, 95% CI = .06 - .26). Study two, which examined current and former athletes (n = 245) from eastern countries (e.g., India, Philippines), replicated this finding for both individualizing (-.37, 95% CI = -.52, -.23) and binding foundations (.12, 95% CI = .02- .25). As predicted, those in the eastern sample scored higher on both binding orientation ( p < .001, d = .61), and “moral disengagement” (p < .001, d = .65). These results suggest that the MDSS-S may measure an orientation towards binding foundations, which many cultures view as moral, not immoral. An increased emphasis on descriptive (rather than prescriptive) research with morally diverse groups seems warranted. Acknowledgments: The author is supported by a Joseph-Armand Bombardier Canada Graduate Doctoral Scholarship from the Social Sciences and Humanities Research Council (SSHRC 767-2012-1381) and by the Heart and Stroke Foundation of Canada and the CIHR Training Grant in Population Intervention for Chronic Disease Prevention: A Pan-Canadian Program (Grant #: 53893). The author would also like to thank Chris Blanchard and Julie Hopper for helpful comments and criticisms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".