The Process of “Becoming” a Certified High-Performance Coach: A Tailored Learning Journey for One High-Performance Athlete
Bibliographic record
Abstract
Although high-performance (HP) coaches’ learning journeys are idiosyncratic and winding, most of these coaches share the characteristic of having rich experiences as athletes. Studies on the career transition of HP athletes to sports coaches reveal a sharp disagreement between these incoming coaches with their practice field experience and national governing bodies responsible for coach education programs about what is needed to be certified. This article presents a tailored initiative to support an HP athlete (Dan) in his process of “becoming” a certified HP coach in the Canadian context. This unique project took shape from a collaborative effort to combine elements of two opposing views on learning: off-the-job versus workplace learning. The article provides details on (a) the coaching context, (b) the main supportive others, and (c) the tools used to document the coaching topics that emerged from Dan’s coaching practice, as well as the learning material used, discussed, and created. When all the above content and materials were carefully organized and placed into folders, a unique “emerging curriculum” was formed and presented to the members of an evaluation committee who agreed that Dan met the HP coach certification criteria.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".