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Record W7161806104 · doi:10.82308/34069

Addressing political inequality: the role of formal education and information campaigns

2016· dissertation· en· W7161806104 on OpenAlexaboutno aff
Valerie Anne Maheo Le Luel

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedPoliticsFormal educationDemocracyArgument (complex analysis)PovertyEducation policyEmpirical evidence

Abstract

fetched live from OpenAlex

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 dissertation is: can formal education and voter education campaigns close the participation gap between advantaged and disadvantaged citizens? In the first article, I investigate the causal and conditional effect of formal education on electoral participation in Canada. I use a propensity score weighting analysis with a longitudinal dataset to evaluate whether the effect of university education on electoral participation varies for individuals coming from advantaged or disadvantaged family backgrounds. The findings show that education has a causal effect on electoral participation, for both the advantaged and the disadvantaged. While the participation-enhancing benefits of education tend to be larger for disadvantaged youth, university education does not close the participation gap between socio-economically advantaged and disadvantaged individuals. In the other two articles, I examine a new type of web information campaign, Voting Aid Applications (VAA). So in the second article, I investigate the effects of a VAA on the political engagement and electoral participation of citizens with varying levels of education. Building on political behaviour research, communication theory, and social psychology, I test alternative hypotheses about the differential effects of VAAs with an innovative randomized field experiment design. The results confirm that the VAA can inform and engage the public. However there is no significant effect on electoral participation. While higher educated users of the VAA learn most from this voting app', the lower educated users become more interested in the elections and more motivated to vote. Thus, across political outcomes, the VAA both decreases and increases political inequalities between privileged and underprivileged citizens. In the third article, I specifically examine whether VAAs influence citizens' electoral decisions. I use a randomized field experiment to evaluate the effect of the Vote Compass on users' electoral preferences during the 2014 Quebec provincial election. The results show that users of the VAA are more likely to form an electoral preference, but this is only the case among thirty-year olds, higher educated, and more politically interested users. At the same time, using a VAA does not affect preferences on Election Day and only impacts party preferences of voters in the short term: the politically uninterested and the older users are more likely to change their party preferences, whereas the thirty-year olds and the politically interested users are less likely to change their initial party preference. Furthermore, the type of recommendation received from the tool does not affect users' vote choice. So contrary to what some have feared, VAAs do not lead to vote switching.

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)0.003
metaresearch head score (Gemma)0.024
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 score0.146
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.036
GPT teacher head0.385
Teacher spread0.350 · 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
Published2016
Admission routes1
Has abstractyes

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