Behind the Scenes: Gender Equality in Local Union Leadership
Bibliographic record
Abstract
This article examines the experiences of local union presidents with respect to gender equality. Based on a qualitative case study of members of union committees dedicated to the status of women and local union presidents working in the Québec education sector, our research points to an unprecedented breakthrough of women serving as local presidents. Nevertheless, the existence of a “triple burden,” the persistence of gender stereotypes and a male union culture, the lack of training and mentoring, and the reliance on solicitation are all hindering the achievement of gender equality. Our research findings also highlight a weak representation of the diversity of social identities within the union organization studied. Summary This article examines the experiences of local union presidents with respect to gender equality. In a context where equity, diversity and inclusion in the workforce are more relevant than ever, this paper aims to contribute to reflections on the democratic deficit of female representation in union organizations. The findings are based on a qualitative case study of members of two union committees dedicated to the status of women and sixteen local union presidents working in the Québec education sector. By gathering the views of these women and men, our research makes a case for the long-neglected place of women serving as local presidents within the union organization studied. It is the efforts made by the unions themselves that have gradually allowed women to enter the traditionally male-dominated union world. Behind this unprecedented breakthrough, however, are gaps that remain in achieving gender equality. Beyond a continuing numerical deficit of women, the difficulty of managing a “triple burden,” the persistence of gender stereotypes, the prevalence of a male union culture, the lack of training and mentoring, and the use of solicitation are major obstacles that prevent women from accessing local union leadership positions. In addition, the representation of diverse social identities (age, family status, race, etc.) is still not well represented at the local decision-making level of the union organization studied, even among elected women. Diversity and gender equality representation remain challenges to be prioritized to ensure a more sustainable and democratic union organization.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.026 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".