Wind Power Deployment: The Role of Public Participation in the Decision-Making Process in Ontario, Canada
Bibliographic record
Abstract
A wider use of renewable energy is emerging as a viable solution to meet the increasing demand for global energy while contributing to the reduction of greenhouse gas emissions. However, current literature on renewable energy, particularly on wind power, highlights the social barriers and public opposition to renewable energy investment. One solution to overcome the public opposition, which is recommended by scholars, is to deploy a collaborative approach. Relatively little research has specifically focused on the role of effective communication and the use of a knowledge-broker in collaborative decision-making. This study attempts to fill this gap through the proposition of a participatory framework that highlights the role of the knowledge-broker in a wind project decision-making process. In this paper, five illustrative wind projects in Ontario are used to highlight the current situation with public participation and to address how the proposed framework could have improved the process. Based on the recommended collaborative framework, perception must shift from the dominant view of the public as “a risk to be managed” towards “a resource that can be tapped”. The developers need to improve sharing what they know and foster co-learning around questions and concerns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".