Wind Energy Development in Ontario: Factors Influencing Deployment and Policy Outcomes
Bibliographic record
Abstract
The goal of this thesis is to gain an understanding of the factors promoting and hindering wind energy development (henceforth WED) from the perspective of communities and developers in Ontario. Ontario arguably has one of the most ambitious policies for WED in the world, centered on the Green Energy and Green Economy Act, 2009 (henceforth GEA). Despite progressing to become Canada’s leading province in installed wind energy capacity, various conflicts and roadblocks to deployment remain evident.\nIn response to gaps identified in literature seeking to understand the factors that impact the (un)successful deployment of wind power, the current thesis provides multiple methodological roadmaps for gaining a more holistic understanding of WED through media analysis. Specific to the Ontario context, the thesis aims to understand the factors that promote or hinder community support for WED. As well, the goal is to understand the factors promoting or hindering the activities of wind energy developers within the province. The aforementioned objectives are addressed through media content analysis and semi-structured interviews respectively.\nWhile results from the media analysis suggests that social acceptance is most strongly impacted by health and economic factors, developer interviews suggest that the elimination of local planning for WED has created major disconnects between developers and host communities. This disconnect has consequently compromised the deployment of the technology.\nThe study makes methodological, theoretical and policy contributions to existing literature on WED. Methodologically, the study demonstrates the efficacy of media content analysis for understanding the temporal evolution of social responses to WED and developing interview instruments. The study also provides an original methodological protocol for the utilization of media analysis to understand WED. Theoretically, the study demonstrates the utility of holistic approaches for teasing out the most salient determinants of WED and policy outcomes. Finally, the study highlights the importance of community engagement in the WED process. As well, it demonstrates the need for detailed policies to guide developers and communities in their engagement with each other.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".