Those who support wind development in view of their home take responsibility for their energy use and that of others: evidence from a multi-scale analysis
Bibliographic record
Abstract
While shifting electricity production to renewable sources is of critical importance in addressing global climate change, the costs of such development are often felt locally. This study explores what leads to support for wind development when respondents are asked to think about three different geographic scales: general, regional and within view of their home. Research was conducted in the Chignecto area of Atlantic Canada, a semi-rural area in which a prominent 15-turbine wind farm was constructed in 2012. A random population mail-out survey achieved a response rate of 40%. Questions explored exposure to wind turbines; support for wind energy development; place attachment; beliefs concerning the distribution of energy and benefits; and demographics. While most predictors of support are significant in bivariate correlations, many commonly used predictors of wind support, such as place attachment or community benefits, disappear or weaken under controls as predictors of support at smaller scales. Novel predictors of support inspired by climax thinking emerged as stronger at more local scales, including support for energy export beyond local needs and agreement that wind turbines provide a reminder of energy use. These results suggest new pathways for understanding support for wind development within the communities most directly affected.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".