Addressing under-representation of women in municipal government: campaign schools, electoral reform, and community-based policy interventions
Bibliographic record
Abstract
This study examines the policy alternatives available to address under-representation of women in one Canadian municipality. Drawing on data from a survey of participants of a local campaign school, this analysis finds that campaign schools are an effective tool for increasing the likelihood that women will run for office. However, the positive effects of campaign schools are not felt evenly by all participants. Women who have more prior election experience, who are older, who do not have young children and who do not report an intersecting marginal identity, are more likely to benefit from campaign schools. Existing literature points to the potential for campaign schools to address individual-level barriers and the need for electoral reform or quota systems to address political barriers. The results of this study show that the primary barriers experienced by participants were community-level barriers. Thus, additional policy interventions such as family-friendly campaign policies and sanctions against harassment in elections are worthy of consideration. This paper concludes that several policy interventions can be employed together to tackle barriers at the individual, community and political levels. These solutions must embrace the diversity of women candidates, so that all forms of under-representation are addressed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".