Exercise Identity and Attribution Properties Predict Negative Self-Conscious Emotions for Exercise Relapse
Bibliographic record
Abstract
Research on exercise identity (EXID) indicates that it is related to negative affect when exercisers are inconsistent or relapse. Although identity theory suggests that causal attributions about this inconsistency elicit negative self-conscious emotions of shame and guilt, no EXID studies have examined this for exercise relapse. Weiner's attribution-based theory of interpersonal motivation (2010) offers a means of testing the attribution-emotion link. Using both frameworks, we examined whether EXID and attributional properties predicted negative emotions for exercise relapse. Participants (n = 224) read an exercise relapse vignette, and then completed EXID, attributions, and emotion measures. Hierarchical multiple regression models using EXID and the attributional property of controllability significantly predicted each of shame and guilt, R² adjusted = .09, ps ≤ .001. Results support identity theory suggestions and Weiner's specific attribution-emotion hypothesis. This first demonstration of an interlinking of EXID, controllability, and negative self-conscious emotions offers more predictive utility using complementary theories than either theory alone.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".