Deference to Legislatures: The Case of the 2018 Ontario Better Local Government Act
Bibliographic record
Abstract
This article analyzes the legislative debates on Ontario’s Better Local Government Act, 2018 through the prism of the reasons why deference should be conferred on choices made by legislatures. It uses the works of British scholar Aileen Kavanagh and Canadian scholar Yasmin Dawood to define a nuanced model of deference focused on the manner in which legislatures have engaged with the problem of rights protection. It provides a six-point framework that summarizes current caselaw and integrates Dawood’s and Kavanagh’s insights. The framework suggests that deference is not warranted on the definition of rights, “manner and form” legislative prescriptions, or partisan self-entrenchment motivations, but is warranted for the resolution of multifaceted issues, informed by governmental expertise, and subject to meaningful parliamentary debates focused on rights and accompanied by participation of electors. The article then carefully analyses the entire parliamentary debates surrounding the Better Local Government Act, 2018. It focuses, as Kavanagh suggests, on the importance of distinguishing between the quality of the decision-making process and the quality of the individual reasoning, the former being the matter that courts should assess, rather than the latter. The article concludes that deference is not warranted in the case of the Better Local Government Act, 2018, since the legislative debates did not focus on expertise, were truncated, dealt minimally with the possible rights violations, and did not offer any participatory possibility. The article also offers some conclusions as to the proper use of parliamentary debates. It concludes that courts could send a signal that deference is owed only when governments and legislatures take rights seriously and provide a rationale for their choices. This may create the right incentives for Parliamentarians to address Charter concerns.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.026 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.012 |
| 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".