Sport Policy Praxis: Examining How Canadian Sport Policy Practically Advances the Careers of Nascent Female Coaches
Bibliographic record
Abstract
The issue of too few females coaching in high-performance Canadian sport contexts is well documented. There is extensive research and programing dedicated to addressing this issue; however, the number of women in high-performance coaching positions within Canada continues to decline. Mentorship is a best practice to advance women into competitive sport coaching roles, and a more recent finding suggests that sponsorship may also be necessary. In this article Canadian national/federal sport policies were analyzed in an effort to better understand how these Canadian sport policies inform and impact the mentorship and/or sponsorship of women coaches. The analysis of four federal government sport policy documents—Actively Engaged, the Canadian Sport Policy, the Coaching Association of Canada’s and the Sport Information Resource Center’s Equity and Access Policy—revealed that none of these pertinent policy documents make explicit reference to mentorship and/or sponsorship programing with the intent to advance more women into high-performance sport coaching positions. As such, the major argument of this study is that the Canadian sport policy sector needs to create policy documents that practically inform programing geared towards nascent female sport coaches and that the voices of female coaches who have been impacted by Canadian sport policies and programing alike, need to be incorporated into these policies.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".