Addressing political inequality: the role of formal education and information campaigns
Notice bibliographique
Résumé
One of the biggest political challenges facing modern Western democracies is political inequality.The gap between the political haves and the have nots is pervasive and even widening.Across developed democracies, socio--economically disadvantaged citizens participate less in politics compared to their more advantaged counterparts.From democratic theory to contemporary public policies, education has been identified as one of the main ways to ensure adequate preparation of democratic citizens and to promote equality of opportunity in politics.However, empirical evidence on the actual democratic benefits of formal and voter education remains mixed.It is the central argument of this dissertation that education has a causal effect on political participation, and that this effect might not be general, but conditional.Indeed, there are strong theoretical and empirical reasons to believe that the democratic benefits of formal education and voter education will vary across social groups.So the question addressed by this journey, I have benefited from the support, advice and incredible intelligence of a number of people.First and foremost, my supervisor, Dietlind Stolle.When I met Dietlind, I was doing my Masters and was a hundred percent sure I did not want to do a PhD.Then, after working with her on several research projects, I discovered how much "fun" doing research could be.She showed me that research could address important questions, speak to salient societal issues, and contribute to policies and programs implemented in the real world.Dietlind showed me that through scientific research, academia could also be part of important societal debates and contribute to social and political innovations.She is truly an inspiration to me, both in how she does research and how she lives: with dynamism and enthusiasm.She has been an amazing supervisor: making sure I had the necessary financial support to conduct my studies and research, and giving me extensive feed back on my work.I feel forever indebted to her for all the amazing opportunities to learn that she has provided me, which have helped me grow both intellectually and personally.The members of my dissertation committee, Elisabeth Gidengil and Stuart Soroka, have been invaluable during the preparation and the writing stages of this dissertation.Elisabeth has been an amazing inspiration to me, and meeting her will leave a lasting impact on my future.When I started my PhD with newborn twins and a partner who was a full--time student, many people wondered if I was crazy (and I literally was asked the question on numerous occasions), which sometimes led me to wonder if I was indeed crazy… or if I could really have it all.Getting to know Elisabeth convinced me that I should not doubt.Through her example as a successful scholar, an amazing mother and an accomplished woman, I got to realize that having it all was possible.It's not easy, but it's worth the ride.She has numerous qualities as a researcher; she is vii rigorous, strategic, and comprehensive.I have learned from her in that sense, and hope I can further develop these abilities as I continue to do research.I would also like to thank Stuart for his many comments, but most specifically for asking the 'confronting questions' and pushing me to think in other ways.His feedback and criticism have certainly made this dissertation stronger.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».