Public Attitudes toward Climate Science and Climate Policy in Federal Systems: Canada and the United States Compared<sup>1</sup>
Bibliographic record
Abstract
Abstract Multilevel governance poses several challenges for the politics of climate change. On the one hand, the unequal distribution of power and interests can serve as a barrier to implementing coherent policy at a federal level. On the other, these features also enable policy leadership among sub‐federal units. In the context of wide variation in climate policy at both national and sub‐federal levels in Canada and in the United States, this paper utilizes an original data set to examine public attitudes and perceptions toward climate science and climate change policy in two federal systems. Drawing on national and provincial/state level data from telephone surveys administered in the United States and in Canada, the paper provides insight into where the public stands on the climate change issue in two of the most carbon‐intensive federal systems in the world. The paper includes the first directly comparable public opinion data on how Canadians and Americans form their opinions regarding climate matters and provides insight into the preferences of these two populations regarding climate policies at both the national and sub‐federal levels. Key findings are examined in the context of growing policy experiments at the sub‐federal level in both countries and limited national level progress in the adoption of climate change legislation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".