A Multistudy Cross-Sectional and Experimental Examination Into the Interactive Effects of Moral Identity and Moral Disengagement on Doping
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it