Challenging energy transition and green jobs: climate policy obstruction across borders
Bibliographic record
Abstract
Critical policy mobility literature still does not usually account for transnational opposition dedicated to push back against policy transfer. To address this gap, we examine the case of policy instruments and discourses in support of energy transition and green jobs. In the 2000s, countries such as Spain, Germany, and Denmark adopted policies to fund renewable energy expansion. The success of feed-in-tariff and other policies served as an example for the promotion of public renewable energy investment in the US. Yet by the early 2010s, Spain and Germany discarded feed-in tariffs and erected regulatory barriers against renewables. An opposing discourse coalition amplified policy controversies in North America and Europe. The Institute of Energy Research (IER) orchestrated such efforts in opposition to president Obama’s renewable energy program. An IER-led campaign focused on the denial of job market claims related to renewable energy (‘green jobs’). Pursuing a multi-site case study of opposition strategy mobility, we examine the organizational and discursive building blocks of this campaign. The campaign against renewable policy and green jobs undermined popular renewable energy transition arguments in times of financial crisis in the United States, and was also mobilized against renewable programs in Canada and Europe.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".