Athlete-centred coaching: An applied example from junior international field hockey
Bibliographic record
Abstract
The extent to which the concept of athlete-centred coaching (Kidman, 2001, 2005; Kidman & Lombardo, 2010) has resonated with the international community has exceeded almost all other coaching discourse over the past decade (Nelson, Cushion, Potrac, & Groom, 2014). As well as featuring prominently in numerous academic coaching texts (e.g. Cassidy, Jones, & Potrac, 2009; Gilbert, 2017; Light, 2017; Pill, 2018), athlete-centred coaching has also been embraced by a large number of National Governing Bodies (NGBs) both in the UK (e.g. England Hockey (Great Britain Hockey, 2015), England Rugby (Rugby Football Union, 2017), England Netball (England Netball, 2007) and the Football Association (The Football Association, 2015)) as well as in numerous other countries around the world including Canada, Finland and New Zealand - to name but a few (Romar, Sarén, & Hastie, 2016). Furthermore, athlete-centred coaching has also been espoused by some highly successful field hockey coaches (e.g. Ric Charlesworth (Light, 2013), Danny Kerry (Richardson, 2015) and Beth Anders (Gilbert, 2017)).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".