Beyond Rhetoric to Understanding Determinants of Wind Turbine Support and Conflict in Two Ontario, Canada Communities
Bibliographic record
Abstract
The literature concerning local opposition to wind turbine developments has relatively few case studies exploring the felt impacts of people living with turbines in their daily lives. Aitken even suggests that such residents are subtly or overtly cast as deviants in the current literature. Our mixed-methods, grounded-theory case study of two communities in Ontario, Canada provides insights about such residents though twenty-six face-to-face in-depth interviews, 152 questionnaires, and basic spatial analysis involving locals who have been living with operating turbines for several years. Despite being neighbours the communities differ on several measures including the spatial clustering of turbines. Opposition is significantly predicted by: Health, siting process, economic benefits, and visual aesthetic variables. Though a majority supports the turbines we focus on the interplay of that majority with those experiencing negative impacts, particularly related to health. We highlight an asymmetry of impacts at the local level on those who oppose turbines, which is supported by rhetorical conflict at multiple scales. The findings point to the need for greater attention to mitigating impacts, including conflict, by understanding how siting policies interact with social processes at the local level.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".