Using MI-LASSO to study populist radical right voting in times of pandemic
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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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".