Democracy During #MeToo: Taking Stock of Violence Against Women in Canadian Politics
Bibliographic record
Abstract
<p>One hundred years after some Canadian women were given the federal franchise, women remain significantly underrepresented in every legislature across Canada. Indigenous, racialized, and young women face additional barriers to participation and are underrepresented far beyond their numbers in the population. While barriers to participation are broad and pervasive, sexual harassment and violence against women in politics - whether in the form of direct threats, implied threats, violent symbolic images, and physical violence - play a significant role in limiting women’s political participation.</p> <p>This report presents non-partisan, evidence-based research on how governments, legislatures, civil society, and non-governmental organizations have addressed the problem of violence against women in politics both within and beyond Canada. The data included in the report draw from extensive Canadian and global research and a select number of interviews with current and former women politicians from across the political spectrum who have bravely spoken out about their experiences of sexual harassment and violence in Canadian politics.</p> <p>The report’s goals are to: (1) enhance public policy discussions on how to combat gender-based violence in Canadian politics and (2) raise awareness about this issue amongst decision-makers and the Canadian public.</p> <p>Current strategies, policies, and legislation from eight countries and eight civil society organizations are included here. These include the world’s first law explicitly and specifically criminalizing violence against women in politics (Bolivia, with similar models adopted in Costa Rica and Mexico). The recently adopted (July 2018) Grievance Scheme in the United Kingdom and the new law adopted (December 2018) by the United States Congress to address sexual harassment are also included, as are recent discussions and changes within the United Nations and European Parliament.</p> <p>Although Canada had taken some steps to address this issue in the past, in the #MeToo era equal and full participation in the democratic process demand further concrete action to solve the problem of violence against women in Canadian politics once and for all. This report offers 12 recommendations for governments, legislatures, political parties, and media, all of which, we believe, have a role to play in combatting this fundamental issue for Canadian democracy and gender equity in politics.</p>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".