Conceptual and empirical advances in analysing policy mixes for energy transitions
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
Energy transitions face multiple barriers, lock-in, path dependencies and resistance to change which require \nstrategic policy efforts to be overcome. In this regard, it has been increasingly recognised that a multiplicity of instruments – or instrument mixes – are needed to foster low-carbon transitions. In addition, over the past few years a broader conceptualization of policy mixes for sustainability transitions has emerged which we adopt in \nthis special issue. Such a broader perspective not only examines the interaction of instruments, but also captures corresponding policy strategies with their long-term targets and pays greater attention to the associated policy processes. It also encompasses the analysis of overarching policy mix characteristics such as consistency, coherence or credibility, as well as policy design considerations. Furthermore, it embraces the analysis of actors and institutions involved in developing and implementing such policy mixes. To explicitly consider these further aspects of policy mixes, this special issue includes fifteen papers with different analytical perspectives drawing on a range of social science disciplines, such as environmental economics, innovation studies and policy sciences. It is our hope that the conceptual and empirical advances presented here will stimulate diverse future research and inform policy advice on policy mixes for energy transitions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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