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Record W7104442258 · doi:10.71781/19672

What voters want : identifying voter preferences for candidates

2021· dissertation· en· W7104442258 on OpenAlexaboutno aff

Bibliographic record

VenuePapyrus : Institutional Repository (Université de Montréal) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsVotingGender gapFocus (optics)Federal election

Abstract

fetched live from OpenAlex

This dissertation is comprised of six standalone articles that provide insights on what type of candidate voters prefer with a particular focus on gender, age affinity, occupation, and political experience (i.e., incumbency). The question of who is elected is one of the most fundamental questions in political science as it pertains to the issue of descriptive representation. The first article presents two novel datasets that I collected. These datasets include information on all candidates in Canadian federal and Ontario provincial elections from 1867 to 2019, and they are the basis for four of the remaining articles in this dissertation. The second article examines whether women get fewer votes in Canadian federal elections. Using the novel data I collected, with over 21,000 unique candidates since 1921 (when the first women were allowed to run for seats in Parliament), we are able to compute precise estimates of the difference in the electoral fortunes of men and women candidates. We demonstrate that while there was a gender gap in the past, the difference between male and female candidates’ vote shares is now statistically indistinguishable from zero. The third article investigates whether women get fewer votes in the Ontario provincial elections. We again estimate the effects longitudinally, using the novel data I collected, from 1902 onwards. The results are very similar to those found for Canadian federal elections. This is important because it shows that our estimates are robust: regardless of the level of government, female candidates are not being discriminated against by voters. While these results might rely on Canadian data, finding similar results at different levels of government enhances the generalizability of my conclusions. The fourth article uses cross-national data from the Comparative Study of Electoral Systems project, covering 853,414 individual voters, 51 countries, 126 elections, and 639 unique leaders. Using this dataset, I test the hypotheses that a leader is more popular among voters closer to them in age and that such voters are more likely to vote for them. I find some support for both hypotheses though the effects are substantively very small. The fifth article asks if candidates who are lawyers get more votes compared to non-lawyers. This paper also leverages the novel data that I collected at the federal level, which includes the occupation and electoral performance of every candidate who ran for office between 1921 and 2015. Our analysis shows that lawyers get more votes than non-lawyers, but that their electoral advantage is very small. The sixth article asks whether incumbents have an electoral advantage and if such an advantage differs across gender. This paper once again uses the novel data that I collected to estimate the electoral advantage enjoyed by incumbents during 9 Canadian federal elections, in 2,739 ridings, from 1990 to 2019. Using a regression discontinuity (RD) design, I compare men and women who have very narrowly won or lost elections on their probability of running again, vote share and probability of winning in the next election. I find that there is an electoral advantage of being an incumbent but that the differences across gender are, with the exception of vote share, not significant. Incumbents are more likely to run again in the next election than their non-incumbent counterparts. Furthermore, women do not suffer an electoral penalty across the three different outcome variables, suggesting that voters are not discriminating against women once they run for office.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.510

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.021
GPT teacher head0.262
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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