When Councillors Sexually Harass: Legislative Sanctions and Gender-Based Violence in Canada’s Municipalities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Previous research has examined whether voters will punish candidates who engage in sexual harassment in national-level elections, revealing partisanship as a strong predictor of electoral punishment. Using original survey data, we evaluate whether the public supports a broader range of sanctions (e.g. apologies, training, and removal from office) that legislatures can impose upon politicians who perpetrate sexual harassment in Canada’s municipalities, a non-partisan context. In the absence of partisan-based motivated reasoning, we find that women are more likely than men to support the removal from office of a councillor who engages in sexual harassment. Respondents who do not believe that sexism is a problem and are skeptical about claims of gender-based violence are also less likely to support punishment in these cases. These findings have relevance for democratic institutions, revealing that sanctions imposed on politicians who perpetrate sexual harassment can help maintain political accountability and restore public trust.
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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 | 0.000 | 0.000 |
| 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 it