Exploring the Gaps between Meanings and Practices of Gender Equity in a Sport Organization
Bibliographic record
Abstract
This article analyses the explanations organizational members used to make sense of the meanings and practices of gender equity. Studying gender equity as an organizational value provided a way of understanding how gender inequity is perpetuated and embedded in the culture of an organization. This study was informed by post‐structuralist feminist theory as it provided a lens for understanding and critiquing the local meanings and production of gendered knowledge, and encouraged discussion of transforming meanings and practices. This study was situated in a Canadian university athletic department in which gender equity was an espoused organizational value, but gender inequities were evident. Data were collected from in‐depth interviews with administrators, coaches and athletes, observations of practices and competitions, and the analysis of relevant documents. These data were coded and categorized using Atlas.ti. Respondents' explanations for the gap between what was espoused and what was enacted centred on two dominant, but contradictory, themes: a denial of gender inequities and a rationalization of gender inequities. These themes suggested respondents often understood inequities as expected, natural, or normal.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".