Climate change policy networks: Why and how to compare them across countries
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
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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