A Multistudy Cross-Sectional and Experimental Examination Into the Interactive Effects of Moral Identity and Moral Disengagement on Doping
Bibliographic record
Abstract
Moral identity and moral disengagement have been linked with doping likelihood. However, experiments testing the temporal direction of these relationships are absent. The authors conducted one cross-sectional and two experimental studies investigating the conjunctive effects of moral identity and moral disengagement on doping likelihood (or intention). Dispositional moral identity was inversely (marginally), and doping moral disengagement, positively, associated with doping intention (Study 1). Manipulating situations to amplify opportunities for moral disengagement increased doping likelihood via anticipated guilt (Study 2). Moreover, dispositional moral identity (Study 2) and inducing moral identity (Study 3) were linked with lower doping likelihood and attenuated the relationship between doping moral disengagement and doping likelihood. However, the suppressing effect of moral identity on doping likelihood was overridden when opportunities for moral disengagement were amplified. These findings support multifaceted antidoping efforts, which include simultaneously enhancing athlete moral identity and personal responsibility alongside reducing social opportunities for moral disengagement.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".