Designed at the top, challenged from the bottom
Bibliographic record
Abstract
Cutting back on greenhouse gas emissions is a key motivation for governments to launch renewable energy support programmes, alongside economic objectives. Designed at national or provincial level, especially support policies for wind turbines have increasingly met with local resistance once materialising into new energy landscapes. With the literature on anti-wind discourses mainly following a single-case study approach focusing on the reasons and origins of local anti-wind sentiments, this paper takes a fresh approach to compare discourses and institutions of pro and antiwind in two beacon jurisdictions for wind energy development in North America and Europe: The Canadian Province of Ontario and the German federal state of Brandenburg. Both frontrunner jurisdictions, however, have experienced massive local anti-wind protest which has scaled up from the local level to a broader high profile debate unfolding at provincial/state level. The paper touches upon a major question in interdisciplinary transformation research: Which discourses lead to the adoption of a pro-renewable political agenda and the resulting institutional design of a decision-making system at the provincial/state scale in the first place? How are local anti-wind discourses scaled up to challenge them?
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".