From Gender Equity to Gendered Assignments? Women and Cabinet Committees in Canada and the United Kingdom
Bibliographic record
Abstract
Abstract This article explores women's access to ministerial power in an important but understudied arena of executive politics: cabinet committees. Specifically, we analyse the gendered patterns in the distribution of cabinet committee assignments in two ‘typical’ Westminster cases, Canada and the United Kingdom, and under two prime ministers, Justin Trudeau (2015–2021) and David Cameron (2010–2016), who both made explicit gender-equity pledges. Informed by previous research into gendered allocation of ministerial portfolios, we investigate the overall extent of women's committee assignments, the gendered dimensions of these assignments and the status of assignments, namely the ‘prestige’ of committee remits, whether committees were chaired by the prime minister and the allocation of chairing responsibilities across committees. In both cases, overall assignment broadly matched shares of women ministers at the cabinet level, but less so during the Conservative–Liberal Democrat coalition in the UK (2010–2015). Women's shares of committee assignments were likely to be lower on ‘masculine’ and ‘high-prestige’ committees compared to ‘neutral’, ‘feminine’ and ‘low-prestige’ committees, but commitment to gender equity is more evident in the Canadian case. While our aim is exploratory and descriptive, we offer several explanations for these patterns, including the supply of women ministers, departmentalism, party branding and the low public profile of cabinet committees.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".