The ‘Social Gap’ in Wind Farm Siting Decisions: Explanations and Policy Responses
Bibliographic record
Abstract
If approximately 80% of the public in the UK support wind energy, why is only a quarter of contracted wind power capacity actually commissioned? One common answer is that this is an example of the ‘not in my backyard’ (Nimby) syndrome: yes, wind power is a good idea as long as it is not in my backyard. However, the Nimby claim that there is an attitude–behaviour gap has been rightly criticised. This article distinguishes between two kinds of gap that might be confused, namely the ‘social gap’ – between the high public support for wind energy expressed in opinion surveys and the low success rate achieved in planning applications for wind power developments – and the ‘individual gap’, which exists when an individual person has a positive attitude to wind power in general but actively opposes a particular wind power development. Three different explanations of the social gap are distinguished, only one of which depends upon the individual gap. In the second section of the article the relevance of our three explanations for policy is considered. It is argued that the different explanations suggest different policy responses and that the success of efforts to increase wind energy capacity may depend on developing a better understanding of the relative significance of the three explanations.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".