The Politics of Intersecting Crises: The Effect of the COVID-19 Pandemic on Climate Policy Preferences
Bibliographic record
Abstract
Abstract Few contemporary crises have reshaped public policy as dramatically as the COVID-19 pandemic. In its shadow, policymakers have debated whether other pressing crises—including climate change—should be integrated into COVID-19 policy responses. Public support for such an approach is unclear: the COVID-19 crisis might eclipse public concern for other policy problems, or complementarities between COVID-19 and other issues could boost support for broad government interventions. In this research note, we use a conjoint experiment, panel study, and framing experiment to assess the substitutability or complementarity of COVID-19 and climate change among US and Canadian publics. We find no evidence that the COVID-19 crisis crowds out public concern about the climate crisis. Instead, we find that the publics in both countries prefer that their governments integrate climate action into COVID-19 responses. We also find evidence that analogizing climate change with COVID-19 may increase concern about climate change.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".