Framing climate change as a human health issue: enough to tip the scale in climate policy?
Bibliographic record
Abstract
Almost four decades of climate science have not yet led to transformative policy change at the pace and scale required to confront the climate crisis. Colleagues in the planetary health community attribute much potential to framing climate change as human health issue in order to create greater impact on policy makers. In this Personal View, we discuss the promise and limitations of this approach by drawing on insights from political science and public policy with regards to the complexity of these contentious policy issues. We argue that we, as academics, have a moral obligation to embrace an active role in the knowledge-to-action (KTA) sphere and that we would be well advised to expand our KTA approach to include evidence-based strategies, such as lobbying or civil resistance. As scientists, we can no longer wait to embrace the realpolitik insights of political science to move our evidence into 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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".