What It Really Means To ‘Think Outside The Box’: Why Foucault Matters For Coach Development
Bibliographic record
Abstract
In this paper I discuss the relevance (and power) of teaching coaches how to question the ‘truth’ of their everyday coaching practices by beginning to ‘think with Foucault.’ My insights derive from my experiences teaching a graduate course called, “Coaching ‘Knowledges’: The Social Dimensions of Performance Sport”, that I designed in 2018 as part of the University of Alberta’s Masters of Coaching degree. More specifically, through my reflections on my past coaching, my present teaching, and the process of writing this paper, I consider how the act of problematizing, as informed by such Foucauldian concepts as docility, discipline, and power-knowledge, can serve to transform coach development and with that, of course, coaching and all that that entails.
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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 | 0.079 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.113 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".