Women representation and organisational characteristics in sport governance: Implications for gender policy and practice
Bibliographic record
Abstract
Drawing on hegemonic masculinity and critical mass theory, this study investigates the representation of women board members in sport governing bodies (SGB) and the extent to which boards can be assigned to subgroups based on the number and share of women board members. The study examines the organisational characteristics of SGBs with low, medium, and high representation of women on the board. Data were gathered from the websites of German national and state sport associations and federations (n = 930), including information about the size and gender composition of the board and several organisational characteristics (e.g. type of sport, headquarter location, membership figures). On average, SGBs have 1.8 women on the board reflecting a share of 20.1%. Three groups of SGBs emerged from the cluster analysis: Organisations with low (0.08 women; share of women: 0.3%), medium (1.63; 18.4%), and high representation of women (3.87; 42.6%) on the board. These clusters differ significantly regarding organisational characteristics. Specifically, SGBs with low representation of women have on average smaller boards and represent non-Olympic sports or ‘typically masculine’ sports. Sport federations are more frequently represented in the clusters with medium and high representation of women on the board. SGBs in the third cluster represent ‘typically feminine’ sports like dancing or equestrian and have the most women and youth memberships. The findings help sport policy makers target respective groups of organisations with interventions to implement gender policies and explain the important role of such policies for attracting new women board members or gaining legitimacy from public institutions.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".