Athlete Social Responsibility (ASR) : a grounded theory inquiry into the social consciousness of elite athletes
Bibliographic record
Abstract
Sport in Canada is struggling to demonstrate that it is accountable, value-based, and\nsocially responsible. Simultaneously, there is a growing consciousness among elite\nathletes to use the power and appeal of sport to affect meaningful social change.\nThrough in-depth interviews, I sought to understand which values and experiences\nmotivated 15 elite Canadian athletes to become involved in social and political activities.\nI employed a grounded theory approach to analyze interview data and to develop the\nAthlete Social Responsibility (ASR) framework.\nMy results show that ASR is grounded in identity and existential development. The\nresearch participants indicated that, early in their careers, sport provided discipline,\ndirection, and purpose, but through the maturation process, they indicated that becoming\nsocially and politically active was instrumental to their personal development,\nperformance, and continued participation in elite sport. They voiced frustration that the\ncurrent sport system does little to encourage such engagement and offered a number of\ninnovative ways in which the current system could adopt an ASR perspective. These\nideas included: developing a resource to help athletes find their cause and link with\nrelated organizations, companies, or charities; helping athletes find ways to connect to\ntheir local communities; and restructuring the Canadian Athlete Assistance Program to\ninclude both performance and ASR criteria.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".