Sharing women's sport coaching journeys to the Canada Games through digital storytelling
Bibliographic record
Abstract
Digital storytelling is a participant-centred method used to illustrate personal narratives and artistic stories by creating a 2–5-minute video using photographs, artwork, voiceover, and/or video clips. This creative method aims to redistribute power between researchers and participants and amplify the voices of historically marginalized individuals, such as women in leadership positions. Within this article, we present digital storytelling as an innovative method for sharing women's stories in sport and leisure. While more women are occupying sports leadership positions, systemic gender inequities remain that impede women from thriving as sport coaches. Situated as part of a larger project that occurred at the Summer 2022 Canada Games, the process of supporting two women in telling their coaching journeys through digital storytelling is shared, including how the stories were conceptualized, designed, refined, and disseminated. Through two individual interviews, one-on-one meetings, and a virtual viewing party, the women's experiences of crafting their digital stories were captured. Insights are shared for utilizing digital storytelling to work with participants in telling their unheard stories, especially when working with individuals who are historically excluded and marginalized. Practical recommendations are provided for using the digital storytelling method alongside interviews within leisure and sport research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".