Gender Relations and the Politics of Addressing Gender-based Violence in Canada: Exploring the Role of Institutions, Interests, and Ideas
Bibliographic record
Abstract
This thesis explores the factors that constrain or facilitate the creation of effective policies for addressing gender-based violence in Canada. Specifically, it examines how government structures, international agreements, and conditions such as global pandemics, influence state responses to violence against women. The analysis combines 3-I framework – which is used in political science to analyze the role of institutions, ideologies, and interest in policy development – with feminist theories and critical discourse analysis. The findings reveal that Canada’s system of governance facilitates policies that (re)produce gendered power differentials, a dynamic which constrains effective policymaking for addressing gender-based violence at the federal, provincial, and institutional levels. The thesis supports post-liberal transnational feminist perspectives that call for community-based local approaches to gender-based violence as opposed to state-based approaches. Furthermore, the research design used in this study underscores the transformative potential of feminist political analysis to the fields of violence prevention and public health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.022 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".