The Alberta Women in Sport Leadership Project: A Social Learning Intervention for Gender Equity and Leadership Development
Bibliographic record
Abstract
This best practice paper describes a Canadian intervention to address the lack of women in sport coaching and leadership roles. While the number of female athletes has increased over the last decades, the opposite is true of female head coaches, both nationally and internationally. The issues influencing this trend are mostly institutional and societal. There is a lack of support systems in place for females attempting to become involved (recruitment) and maintain their involvement (retention) in coaching. The Alberta Women in Sport Leadership Impact Program (AWiSL) takes a community of practice approach to increase gender equity and leadership diversity in Alberta sport organizations. The AWiSL began in October 2017 and continues until early 2020. There are currently 6 mentors and 12 sport leaders from Alberta sport organizations, who engage in monthly meetings to learn and participate in the co-creation of knowledge to meet the project outcomes, which include the planning and implementation of initiatives for their individual sport organizations, all in the service of supporting gender equity. Descriptions of specific activities thus far are presented as well as information about the how to of conducting such an intervention. Various challenges and lessons are discussed. The description of the AWiSL and ongoing program evaluation aims to support other organizations seeking an example of an initiative to create equitable coaching and leadership opportunities, and to create change.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".