Planning, practice and performance: the discursive formation of coaches' knowledge
Bibliographic record
Abstract
This paper takes as its starting point that coaches' efforts at planning their athletes' training are a complex practice involving so many variables that the logic of how they all ‘fit together’ to produce a peak performance is never obvious or clear. However, many coaches operate as if their athletes' training programmes can be assembled in a coherent, rational manner, or as if ‘systems’ exist to make planning an orderly sequence of steps or stages. Drawing on the work of Foucault, and his call to problematise the development and formation of dominant practices, I examine in this paper the discursive construction of contemporary planning practices used by middle- and long-distance running coaches. Further, I discuss how coaches' knowledge of planning is enmeshed within relations of power, that weaved in discourses, and imprinted on athletes' bodies and bodily practices, attempt to assert the ‘truth’ about the practice of planning.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.128 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 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".