Using MI-LASSO to study populist radical right voting in times of pandemic
Why this work is in the frame
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Bibliographic record
Abstract
As immigration issues waned in salience during the COVID-19 pandemic, populist radical right (PRR) parties repositioned themselves by politicizing various pandemic policies. In light of this changing political landscape, scholars have analyzed what factors are associated with PRR voting. Yet, most studies focus on small sets of covariates that could easily ignore other key determinants. To address this limitation, we use MI-LASSO logistic regression, which is a more inductive data-driven approach that can incorporate a huge number of covariates. Our research analyzes the key determinants of voting for the People’s Party of Canada—a PRR party that rose rapidly during the pandemic. Using the 2021 Canadian Election Study dataset ( N = 14,841), we confirm that PRR voters in the pandemic were both protest and policy-oriented voters. They were protest voters since anti-establishment attitudes consistently correlate with their vote choice. On the other hand, PRR voters’ policy concern was about pandemic policies rather than immigration, as nativist attitudes never emerge as key determinants. Additionally, we uncover that the ideological placement of the mainstream right party and the defense of hate speech are strong correlates, while conventional variables like sociodemographics are not. These findings enrich our understanding of PRR voting during the pandemic.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it