Access to Justice: Recommended Reforms to the Ontario Justice System Using the Green Energy Act as an Example
Bibliographic record
Abstract
Methods: A document was prepared and sent by a lawyer, Alan Whiteley, to Ontario government officials that identified the main concerns with the Green Energy Act and its impact on the rights of citizens. The Act had been introduced in 2009 in efforts to make Ontario a world leader in “green” energy production. With the passing of the Green Energy Act, a number of statutes were also amended in order to achieve this goal; they reduced impediments to the approval of industrial wind turbine projects. The letter in its entirety is included in this paper. Mr. Whiteley had been involved in a legal case initiated by a not-for-profit organization that argued that the regulatory changes impacted the rights of citizens. Documents such as those submitted through that court filings, such as Factums and Affidavits provided by Ontario residents, and other documents are referenced. Objectives: The goal of the letter was to affect modernization of the justice system to improve access to justice, citizen rights and animal protection. Results: The letter identified and described changes to Acts and policies, gave examples of impacts, and offered possible reform proposals that would allow citizens fair access to justice and protect their rights. These proposals were solutions through changes to the legal system. No reply to the letter was received from any of the government officials, increasing concern regarding the value of the voice of the public.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".