Bibliographic record
Abstract
Objectives: To explore the development and implementation of Ontario’s Green Energy Act and the outcomes on social justice and risk of harm to Ontario residents. To provide examples of government actions taken to achieve its goals and the occurrence of consequences, whether intended or unintended. Methods: In Ontario, many legal cases have been filed due to concern regarding the impact of industrial wind turbines on people and the environment. The contents of this article have primarily been taken from the documents filed during an Application for a Judicial Review that examined the process of approval of industrial wind turbines in Ontario. References to support the content of this article also include: evidence derived from other legal cases, government communications including records obtained by Freedom of Information requests, peer reviewed literature, and other sources. Results: Evidence is presented that suggests the government erred by creating an inflexible policy/statute that ensured that industrial wind turbines would be approved, erected and become operational at any cost. It provides examples of government actions taken to achieve this position that are contrary to widely held fundamental principles of administrative law and governmental legitimacy. Recommendations are provided for mitigating some of the outcomes of a government policy and preventing impacts on social justice from happening again.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".