Social Theory for Coaches: A Foucauldian Reading of One Athlete's Poor Performance
Bibliographic record
Abstract
This paper explores my “sense making” when a male cross-country runner was coaching performed below expectation. My initial understanding of his poor performance was to blame him for “lacking” the appropriate mental toughness. As a result, I located the “problem” within him and subsequently ignored many of my own taken-for-granted coaching practices as perhaps contributing to his poor performance. In this paper, provide an alternative reading of my judgement of this athlete's poor performance through Michel Foucault's theory of disciplinary power. conclude by suggesting that many everyday coaching practices may have a number of “hidden” or problematic consequences attached to them that coaches should consider in an effort to evaluate the effectiveness of their coaching and to enhance their athletes' performances.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.012 | 0.108 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| 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".