Using an action learning approach to support women social learning leaders’ development in sport
Bibliographic record
Abstract
This paper examines an adapted action learning approach to develop four social learning leaders. The Alberta Women in Sport Leadership Impact Program is a social learning intervention with the goals of supporting women in developing their leadership capabilities and increasing gender equity across sport. To support the facilitation of this initiative, four social learning leaders engaged in action learning to develop their leadership capabilities and facilitation skills. Considering facilitators’ development experiences have not been extensively explored in the context of action learning and social learning working in combination, examining the implications of an action learning approach for women social learning leaders’ development was warranted. We used an interpretive qualitative methodology to interview and observe the four social learning leaders to gain insight into their experiences building their facilitator capabilities and the implications of coupling an action learning and social learning approach for development. The participants discussed the importance of developing self-awareness, engaging with and embracing uncertainty, and building trusting relationships. The findings from this action learning focused initiative highlight the importance of social learning opportunities for women to create networks and spaces where they can safely feel vulnerable and subsequently develop their leadership capabilities.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| 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".