‘I hurt myself because it sometimes helps’: former athletes’ embodied emotion responses to abuse using self-injury
Bibliographic record
Abstract
In this paper, narrative analysis using a story analyst approach is used to explore how three former athletes (i.e. amateur and elite swimmers) self-managed their abuse experiences post-sport with a focus on the use, and meaning, of ‘indirect self-injury’ forms. Using the concept of ‘emotion work’, the swimmers’ stories show how they reconfigured the emotions associated with the legacy of abuse by using indirect self-injury (e.g. eating disorder; abuse of prescription medications; excessive alcohol use; promiscuity) as embodied resources, after they were left to fend for themselves post-sport. As acquired resources within their self-stories, indirect forms of self-injury assisted them to reconfigure the trauma of abuse into something that was more manageable (i.e. ‘emotion work’). While ‘emotion work’ was storied as successful for the three swimmers in the short term, the potential long-term health consequences of self-injury (i.e. kidney disease; liver damage; unwanted pregnancy, sexually transmitted diseases, death) were imminent. These findings highlight the need for sporting stakeholders to extend their duty of care to athletes, particularly abused athletes, post-sport.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".