Understanding developer perspectives and experiences of wind energy development in Ontario
Bibliographic record
Abstract
In the Province of Ontario, Canada, aggressive policy promoting wind energy development has led to both rapid development and intense stakeholder conflicts. Focusing on developers, key stakeholders largely hidden in the extant research literature and the perspectives of other stakeholders drawn from secondary sources, this paper presents original primary research to help fill this knowledge gap. Based on semi-structured interviews with established and active developers in the Province, we find that feed-in tariffs have arguably been the strongest driver of developers successfully getting turbines up and running. Yet, legislative and policy attempts to reduce delays and smooth the development process have often complicated the development process. Developers recognise and often agree with community viewpoints that the process as framed by Ontario’s policy environment forestalls cooperative development, particularly with respect to community engagement. While developers are supportive of better community engagement, they feel constrained by policy-related barriers. Findings from the study show that communities will only be engaged in projects to the full extent possible if developers take the initiative to transcend regulatory requirements for public engagement. The study concludes with useful lessons for jurisdictions transitioning to low or zero emissions energy technologies. Specifically, it supports recommendations for alternative policy approaches including consideration of policy specificity around economic benefit destitution, and community engagement and ownership of projects.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 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".