Informing Coaches’ Practices: Toward an Application of Foucault’s Ethics
Bibliographic record
Abstract
Progress and improvement in sport is often the result of some type of change. However, change for change sake is not always beneficial. Therefore, to be an effective ‘change agent’ a coach must be able to problematize his or her actions and assess why or why not a change might be needed. Accordingly, helping coaches become active problematizers is vital to the change process. Toward this end, we present in this paper our reflections as coach developers and coaches who considered how to apply Michel Foucault’s understanding of ethics to make self-change a positive force for enhancing athletes’ experiences. We then conclude by suggesting how coach developers might begin to incorporate Foucault’s work into the development of coaches capable of producing change that matters.
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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 | 0.094 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.080 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".