“A Strong Man Is Direct and a Direct Woman Is a Bitch”: Gendered Discourses and Their Influence on Employment Roles in Sport Organizations
Bibliographic record
Abstract
Despite increasing numbers of women in senior sport management positions over the past 30 years, men still remain dominant in these roles, indicating a level of gender inequity within sport management. It is often assumed within sport organizations that women are well-matched for lower level management roles, whereas men are more suited to senior management roles. In order to understand perceptions held about women's and men's abilities related to sport management, it is necessary to understand and then analyze discourses, or dominant forms of knowledge, that influence various employment roles in sport organizations. After analyzing organizational documents and transcripts from interviews with 35 employees from three national sport organizations in England, it was found that senior management roles were heavily dominated by discourses of masculinity that are linked to men and are highly valued in sport organizations. In contrast, women and discourses of femininity are associated with employment roles that are undervalued within organizations. There is, however, the potential for resistance to these discourses on a number of levels and this is discussed with relation to one organization's commitment to change “taken for granted” assumptions about gendered employment roles in sport management.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".