Exploring a Women-Only Training Program for Coach Developers
Bibliographic record
Abstract
The following practice paper introduces an innovative women-only training program for coach developers in a Canadian provincial sport organization. The dearth of women in coaching and sport leadership positions informs the program as a whole and the participant perspectives on what is working, in practice, for them specifically in a way that could support future sport leaders interested in increasing gender equity in their sport organizations and leadership skills in their female leaders. The aims of the coach developer program are two-fold: to promote women in leadership and to create a social learning space for women to connect and support each other in their leadership development. The purpose of this practice paper is to discuss the supports that have enabled the facilitation of this program and to explore the value of a women-only training program. Two women (out of a total of 10) participating in the program and two leads facilitating the program were interviewed for their perspectives. The lessons learned touch on the types of value that were created (immediate, potential, and applied) and the specific supports (micro, meso, and macro) that enabled the facilitation of the program. Finally, the authors discuss additional considerations (e.g., consistent buy-in from the organization is needed) with practical insights in the hopes of inspiring other sport organizations to implement similar initiatives for promoting women in leadership and coaching in 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".