Bibliographic record
Abstract
Positive coaching has traditionally been defined and understood through a modernist lens (Smoll & Smith, 1987; Thompson, 1995, 2003) and a combination of privileged scientific knowledges. One effect of this is that coaches' problemsolving approaches tend to disregard the complex social, and relational dimensions of coaching (Nash & Collins, 2006) and ignore how problems get selectively framed and named (Lawson, 1984). As a result, many problems in sport remain misunderstood or solved ineffectively. Drawing on the work of Michel Foucault we critique these reductionist understandings of effective and ethical coaching and argue that for coaches to become a positive force for change, they must engage in an ongoing critical examination of the knowledges and assumptions that inform their problem-solving approaches. Further, we conclude that for coaching to become a respected profession worthy of deep and intelligent thought, it is vital that coaches carefully consider the effects produced by the way they solve problems.
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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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.065 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".