From Center Stage to the Sidelines: What Role Might Previous Athletic Experience Play in Coach Development?
Bibliographic record
Abstract
Traditionally, playing experience in sport has been used as a springboard into the coaching profession. Specifically, playing experience has been discussed in research as facilitating the transition into early coaching roles, fast-tracking through coach education programs, and being viewed as a desirable factor in high-performance sport. However, explorations into the intricacies that make this playing experience so valuable have been minimal. Thus, this Insights article is meant to foster discussion within the coach research community regarding the role of playing experience in coaching pathways from a position perspective. This unique area of inquiry may offer insight to those concerned with coach pathways, coach development, and coach education. To promote this discussion, the following article will present some avenues through which previous playing experience could be explored. In addition, the authors will present a study that was conducted with high-performance head ice hockey coaches who formerly played goaltender and offer interesting directions for future research inquiries. Notably, the authors will consider playing experience in connection with career advancement, potential implications for hiring processes, considerations for coach education, and possible barriers to coaching opportunities.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".