Winning-at-all-costs: high-performance athletes learn to conform to the sport ethic
Bibliographic record
Abstract
Athletes ‘learn’ to conform to the extensive pressures and expectations within the culture of sport; however, an understanding of the process by which athletes learn to conform to the sport ethic is not well known. As high performing athletes’ over-conformity to traditional sport values has been linked to negative health consequences (e.g. overtraining, playing while injured, disordered eating, engagement in hazing, chronic pain management, performance-enhancing substance abuse and risk for maltreatment), it is crucial that we better understand the process by which athletes learn to conform to these extreme expectations within sport. Therefore, the aim of this study was to examine high-performance athletes’ experiences of conforming to the sport ethic and the process by which they adhere to these dominant sport values. A total of 13 retired, competitive, women athletes from a variety of sports participated in this study and engaged in a 1-to-2-hour semi-structured interview. Data were analysed thematically. Generated themes include the development of athletes’ conformity to the sport ethic through their initial sport engagement, modelling and observing, positive reinforcement and fear of repercussion. Additionally, athletes experienced both maladaptive and adaptive long-term outcomes stemming from their learned sport ethic which stuck with them post-sport involvement. Findings are interpreted using Bandura’s theoretical perspective of observational learning and future directions are recommended.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".