Institutional disruption and women’s substantive representation: the Senate of Canada as a case study
Bibliographic record
Abstract
This article asks how new (gendered) rules are established within changing institutions. We focus on the Canadian Senate, which underwent reforms to its appointment process in 2014–2015. As corresponding institutional changes were established, the Senate’s rules and norms were disrupted. We ask: did feminist actors leverage the state of flux in order to regender institutional rules? We examine the case of Bill C-65 (2018), legislation that strengthened workplace violence and harassment rules in federal workplaces, including the Senate. Using content analysis of discursive texts and qualitative interviews, we identify the critical actors who helped reform those gendered rules and we argue that newness—both new actors and new rules—was a factor in successfully establishing gender-sensitive policies. Based on this case, we suggest that institutions undergoing reforms writ large present opportunities for feminist actors to establish new rules and norms within them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".