Learning how to coach: the different learning situations reported by coaches
Bibliographic record
Abstract
This chapter aims to identify and describe the different learning situations reported by coaches, and to gain some insight into their preferred sources of knowledge. It starts by outlining the background to classifying sources of coach learning as formal, non-formal and informal (Nelson <i>et al.</i>,2006). The chapter then moves onto the main section which identifies the range of learning situations experienced by coaches. This section of the chapter draws heavily on a study that used 35 interviews of community youth ice hockey coaches in Canada (Wright <i>et al.</i>, 2007); the main issues identified in the Canadian study are transferable to other sports and settings. Quotes have been used from Wright <i>et al.</i>'s (2007) interviews for coaches to explain, in their own words, how they perceive their learning; in addition, edited sections of their explanatory text have been utilised and referenced throughout. The chapter concludes by considering the learning preferences of coaches from a <i>variety</i> of sports (Erickson <i>et al.</i>, 2009).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".