Critical Masses and Gender Diversity in Voluntary Sport Leadership: The Role of Economic and Social State-Level Factors
Bibliographic record
Abstract
Gender equality in leadership positions is important for sport organizations to achieve economic and social sustainability. Based on a multi-level framework, this study examines spillover effects from economic and social state-level factors in sport organizations’ environment on critical masses of women on their boards (in terms of share and numbers) and board gender diversity (reflected by different types of boards). Data of national and regional sport governing bodies in Germany were collected (n = 930), with variables capturing organizational characteristics (e.g., board composition) and economic and social factors at the state level. The results of regression analyses show that women’s attainment in tertiary education increases the likelihood of a critical mass of at least 30% women on the board, and a higher divorce rate increases the likelihood of a critical mass of three women on the board. Sport organizations in states with a higher gender wage gap are more likely to have balanced boards, indicating that volunteering might be a substitute to paid work. The findings suggest that the presence of women in sport leadership is affected by economic and social conditions in the organizations’ geographical surroundings and that spillover effects occur from the state level to the organizational level.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".