Capacity for gender equity initiatives: a multiple case study investigation of national sport organisations
Bibliographic record
Abstract
To better understand mechanisms for gender equity in sport, the critical elements of capacity of Canadian national sport organisations (NSOs) to implement gender equity initiatives, and the relative strengths and challenges of those elements, were investigated. Environmental factors perceived to influence that capacity were also explored. The study was framed by Hall et al.’s (2003) multidimensional model of organisational capacity. Instrumental case studies were used to examine and compare the capacity of three NSOs engaged in addressing gender equity in their sport through their respective initiatives designed to increase the engagement of women in sport as athletes, coaches, and officials. Semi-structured interviews (n = 15) were conducted with board members and staff across the three NSOs. Several common capacity strengths (e.g., knowledgeable and experienced staff, dedicated funding) and challenges (e.g., limited staff, constraints in external communication) were identified. Capacity elements unique to each NSO were also uncovered. Environmental factors influencing the NSOs’ capacity to implement their respective gender equity initiatives included the broad political climate, access to volunteers, and availability of additional funding sources . The findings address the call for further evidence of critical organisational practices for enacting gender equity, with a particular focus on NSOs, and framed by a multidimensional model of organisational capacity and environmental influences. The findings have implications for being aware of the capacity of NSOs to address government policy and directives for gender equity in sport, and for maintaining and building capacity to implement gender equity initiatives.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".