Timely climate proposals. Discourse networks and (dis)continuity in European policies
Bibliographic record
Abstract
How do discursive fields influence support for climate policies? The European Green Deal (EGD) has gained media attention in part because it was presented as a cross-sectorial strategy aiming to ‘transform the European economy’. Our analysis focuses on two specific policy proposals of the EGD: the carbon border adjustment mechanism and the reform for a greener Common Agricultural Policy. By comparing their discourse network structure, we aim to understand policy (dis)continuity introduced with the EGD. We use an original longitudinal dataset and discourse network analysis to map framing dynamics over time and understand how particular frames can gather support in policy networks. Our study shows that two elements favor policy change, namely the resonance of new frames with the discursive field and the presence of brokers connecting previously disconnected actors or coalitions. This paper is relevant for scholars interested in the discursive layer of policy networks as well as (dis)continuity in policy debates.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".