Agricultural landowner perspectives on wind energy development in Alberta, Canada: insights from the lens of energy justice and democracy
Bibliographic record
Abstract
The political and economic landscape of Alberta, Canada, is deeply affected by fossil fuel extraction, thus limiting progress toward energy transition. Although transition is slowed by resistance to renewable energy technologies, public perspectives on these projects are diverse, with localized sensitives that are often not well understood. To improve our understanding of support and opposition to wind energy development, we draw on concepts of energy democracy, distributive and procedural justice. Utilizing a factorial survey experiment, and latent class analysis to measure these concepts with a sample of 401 large-scale agricultural landowners, we identify three distinct groups of individuals with unique preferences that are grounded in how individuals view and support wind energy. Contrasting most respondents with moderate views on wind projects, we identify a distinct group of supportive landowners when community benefits are well defined. A third group is defined largely by opposition to wind energy whereby justice concerns are associated with distancing their land from the impacts of wind turbines. Our conclusions identify the value of careful and transparent project design in consultation with local communities and affected landowners to avoid opposition noted here and in previous studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 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".