Calibrating climate change policies: the causes and consequences of sustained under-reaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We apply insights from the recent literature on disproportionate policy reactions to the case of climate change policy-making. We show when and why climate change exhibits features of a sustained under-reaction: Governments may react to concerns about climate change not through substantive change but by efforts to manage blame strategically. As long as they can avoid blame for potential negative policy outcomes policy-makers can act to deny problems, or implement only small-scale or symbolic reforms. While this pattern may change as climate change problems worsen and public recognition of the issue and what can be done about it alters, opportunities to manage blame will still exist. Governments will only revert to more substantive interventions when attempts to fatalistically frame the problem as unavoidable fail in the face of increased public visibility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it