Gender and Emotional Reactions to Sexual Misconduct Allegations Against Councillors: An Experimental Study in a Low-information, Non-partisan Context
Bibliographic record
Abstract
Public accounts of gender-based violence by elected officials have become increasingly common at all levels of government. Given that media and public attention to the problem are also on the rise, it is important to understand how the public reacts to such stories as it is voters who ultimately evaluate this information and determine how it informs future voting decisions. This research note considers reactions to stories of sexual assault and harassment (SAH) in the low-information and non-partisan setting of municipal politics in the province of Ontario, Canada. Replicating and expanding upon previous studies conducted in national partisan electoral arenas in a local government context, we consider answers to a survey experiment that asked respondents how they would react when informed of a case about a local politician in their community being accused of SAH. We assess whether men and women respond differently to stories about SAH; whether reactions are conditional on councillor gender and; whether councillor gender leads to different reactions for women and men. Experimental data come from a survey of Ontarians (N = 4,000) collected at the time of the 2022 municipal elections. Results reveal that both the gender of voters and councillors affects reactions to stories of SAH.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".