Addressing political inequality: the role of formal education and information campaigns
Bibliographic record
Abstract
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.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".