Project CoachLearn - Report#3 - The context and motivations for the collection and application of sport coaching workforce data in 5 European countries
Bibliographic record
Abstract
An increasing trend in the United Kingdom (UK) has been to argue for, and attempt to use, more detailed coaching workforce data to inform sport coaching system and programme development (e.g. Lynn & Lyle, 2010; North, 2009; Sports Coach UK, 2008, 2012; Winder & Townend, 2010). There have also been similar pockets of research activity internationally notably in Australia (Dawson, Wehner, Phillips, Gastin, & Salmon, 2013) and Canada (Reade et al., 2009) although their connection to the policy process is less clear. Recently there have been a number of UK centred proposals to the Europe Commission to explore the possibilities of opening up these methodologies to European countries and beyond, from Leeds Beckett University as part of its partnership with the International Council for Coaching Excellence (ICCE), the European Coaching Council (ECC), and other European partners. This includes the 2011 Preparatory Action in the Field Of Sport bid CoachNet, written up in a final report by Duffy, North, Curado, and Petrovic (2013), and the 2014 Erasmus + bid CoachLearn, of which this project forms a part. As a result of early investigations related to the CoachLearn project it became clear that the UK context and motivations for the development and application of specific research methodologies, and the collection and use of coaching workforce data were fairly unique. This meant that some important assumptions underpinning recent successful bids with regard to coaching workforce data methodologies and their application across Europe required further examination. This paper explores the context and motivations for, and applications of, the collection and use of coaching workforce data in five European countries: Finland, Germany, Hungary, the Netherlands, and the UK to determine whether a common methodology and tools to underpin coaching workforce data collection is relevant, useful, and viable. In undertaking this work the paper faces into comparative issues concerning centralised ‘good practice’ frameworks, evidence based decision making, performance management, research and research methodology, which could inform wider debates both inside and outside sport.
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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 | 0.098 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".