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Enregistrement W7104442258 · doi:10.71781/19672

What voters want : identifying voter preferences for candidates

2021· dissertation· en· W7104442258 sur OpenAlexaboutno aff

Notice bibliographique

RevuePapyrus : Institutional Repository (Université de Montréal) · 2021
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueElectoral Systems and Political Participation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPoliticsVotingGender gapFocus (optics)Federal election

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,256
Score d'incertitude au seuil0,510

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,011
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0020,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,262
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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