Reactions to policy action: socio-political conditions of backlash to climate change policy
Bibliographic record
Abstract
Public policymaking on issues requiring ambitious yet socially and economically costly action can face backlash from target groups and wider audiences, threatening policy adoption and durability. As an abrupt negative reaction to policy action, backlash is challenging to study and requires distinctive analytical approaches. This is especially pressing for climate change mitigation policy, which faces growing yet dispersed empirical experiences of backlash. We develop a framework to study the socio-political conditions (economic, cultural, practical) under which backlash to climate policy occurs to enable comparative empirical analysis. We posit that backlash arises from significant incongruence between policy action and its socio-political context across one or more of these dimensions. We illustrate this approach using three cases of backlash to carbon pricing policy in Canada, France, and Mexico, revealing different ways in which incongruence can arise. Our analysis highlights the need for configurational explanations and a policy-in-context perspective when studying contentious reactions to policy action.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".