The cyclical relationship between physiology and discipline: one endurance running coach’s experiences problematizing disciplinary practices
Bibliographic record
Abstract
There have been numerous calls by coaching researchers for Foucauldian-informed coach developers to help coaches change their practices to be less reliant on discipline’s techniques and instruments. In this paper, we explored what it might mean for a Foucauldian-informed coach developer to work collaboratively with a male university endurance running coach as he learned how to problematize the use of discipline. More specifically, we examined some of the barriers, challenges, and opportunities that the coach experienced as he attempted to learn, in collaboration with the first author, how to question the unintended consequences of discipline’s techniques and instruments and rethink the “total effects” of his coaching practices. The results revealed that the coach was able to show a degree of problematization, however, in the field the deep-rooted connection between endurance running, physiology, and discipline made coaching for him in a less disciplinary way a challenge. To conclude, Foucauldian-informed coach developers working in sports where physiology is the predominant sport science could use specific pedagogical strategies that work with and explicitly complicate the strong cyclical relationship between discipline and physiology to help coaches implement practices that are less dominated by, not absent of, physiology.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".