COVID-19, poverty reduction, and partisanship in Canada and the United States
Bibliographic record
Abstract
Abstract Poor people proved especially vulnerable to economic disruption during the coronavirus disease (COVID-19) pandemic, which highlighted the importance of poverty reduction as a policy concern. In this article, we explore the politics of poverty reduction during the COVID-19 crisis in Canada and the United States, two liberal welfare-state regimes where poverty reduction is a key policy issue. We show that, since the beginning of the pandemic, policies likely to reduce poverty significantly have been adopted in both Canada and the United States. Yet, this poverty reduction logic has emerged in different ways in the two countries—with the United States embracing more significant departures from its policy status quo. This situation leads us to ask the following question: in each country, what are the political conditions under which public policies susceptible of reducing poverty are enacted? To answer this question, we study the politics of poverty reduction both before and during the pandemic, as we suggest that grasping the evolution of partisan and electoral patterns over time is necessary to explain what happened during the pandemic, whose impact is closely related to how it interacts with such patterns. Our analysis suggests the need to consider more carefully the impact of both crises and partisanship on social policy, including poverty reduction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".