A Case-Control Study of Support/Opposition to Wind Turbines: The Roles of Health Risk Perception, Economic Benefits, and Community Conflict
Bibliographic record
Abstract
Despite considerable quantitative case study research on communities living with turbines, few have studied the roles played by the perceptions of: health risk, economic benefits/fairness, and intra-community conflict. We report the findings from a case-control survey which compares residents living with/without turbines in their community to understand the relative importance of these variables as predictors of turbine support. Ontario is the context for this study as it is a place where the pace of turbine installations is both very high and extremely politicized. As expected 69% of residents in the case community would vote in favour of local turbines yet surprisingly, only 25% would do so in the control community. Though the literature suggests that aesthetic preferences best predict turbine support the key predictors in this study are: health risk perception, community benefits, general community enhancement, and a preference for turbine-generated electricity. Concern about intra-community conflict is high in both the case (83%) and control (85%) communities as is concern about the fairness of local economic benefits (56% and 62%, respectively); yet neither is significant in the models. We discuss the implications of these findings particularly in terms of the consequences of a technocratic decide-announce-defend model of renewable facility siting.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".