A Case Study of a Parasport Coach and a Life of Learning
Bibliographic record
Abstract
The complex process of sport coaching is a dynamic and evolving practice that develops over a long period of time. As such, a useful constructivist perspective on lifelong learning is Jarvis’ (2006, 2009) theory of human learning. According to Jarvis, how people learn is at the core of understanding how we can best support educational development. The purpose of the current study is to explore the lifelong learning of one parasport coach who stood out in his feld, and how his coaching practice evolved and developed throughout his life. A thematic analysis (Braun & Clarke, 2006) was used to extract themes and examples from three two-hour interviews as well as interviews with key collaborators in his coaching network. The findings reveal a coach whose coaching practice is founded on pragmatic problem solving in the face of a lack in resources; an investment in formal and nonformal adapted activity education at the start of his parasport career; and observation, communication, and relationship-building with his athletes and the parasport community. Suggestions are provided for coach developers on how they might invest resources and create learning opportunities for coaches of athletes with a disability.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".