Climate change policy networks: Why and how to compare them across countries
Bibliographic record
Abstract
Why do some countries enact more ambitious climate change policies than others? Macro level economic and political structures, such as the economic weight of fossil fuel industries, play an important role in shaping these policies. So do the national science community and the national culture of science. But the process by which such macro-structural factors translate into political power and national climate change policies can be analyzed through focussing on meso level policy networks. The Comparing Climate Change Policy Networks (COMPON) research project has studied climate change policy networks in twenty countries since 2007. Along with some findings, this paper presents some methodological challenges faced and the solutions developed in the course of the project. After a presentation of the project, we first outline some practical challenges related to conducting cross-national network surveys and solutions to overcome them, and present the solutions adopted during the project. We then turn to challenges related to causal explanation of the national policy differences, and propose Qualitative Comparative Analysis as one solution for combining different levels of analysis (macro and meso) and different data types (quantitative, network and qualitative).
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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 | 0.045 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.034 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".