Addressing Violence and Harassment in Canada’s Senate: Critical Actors and Institutional Responses
Bibliographic record
Abstract
The global movements of #MeToo, #TimesUp, and #BlackLivesMatter have brought the issues of gender and race-based violence into the public domain. The realm of politics is no exception. Over the last several years (and predating #MeToo), Canadian politicians and staffers at all levels of government and from all political stripes have faced sexism, racism, homophobia, harassment, and threats of violence from members of the public and from their colleagues. Since the 2019 federal election, this has included an armed trespasser apprehended on the grounds of Rideau Hall who made threats against Prime Minister Justin Trudeau, the vandalism of Minister Catherine McKenna’s constituency office window which was spray-painted with a vile, misogynistic word, and the street harassment of NDP leader Jagmeet Singh that was widely shared on social media. Although white, heterosexual, cisgender male politicians and staffers also experience violence, women, Black, Indigenous and persons of colour (BIPOC) and members of the LGBTQ+ community are disproportionately more likely to be on the receiving end of such acts and threats both on social media and in real life.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.085 | 0.024 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".