Securing Accountability through Commissions of Inquiry: A Role for the Law Commission of Canada
Bibliographic record
Abstract
Focusing on the Government of Ontario's unwillingness to call a public inquiry into the death of Dudley George, an Aboriginal protester, and the Government of Canada's willingness to interfere with an inquiry into the deployment of Canadian forces to Somalia, this article argues that governments appear increasingly reluctant to support a commission of inquiry into a public crisis even where it can serve as a catalyst for addressing larger and more pressing concerns of institutional and policy reform. It first addresses "start-up problems" associated with the fact that the decision to appoint a commission of inquiry lies within the sole discretion of cabinet. It also canvasses incentives and disincentives on political actors and the media to call for the establishment of a commission of inquiry. It then examines "shut down problems" associated with governmental efforts to prematurely end or restrict a commission's activity. Borrowing several features from the Independent Counsel and the Inspector General Models in the United States, the article proposes that the Law Commission of Canada act as a permanent base of operations for federal commissions of inquiry, and suggests that commissions of inquiry adopt non-confrontational methods and procedures that encourage governments and other parties to sit down and engage in a constructive exercise of fact-finding, polity formulation and structural reform.
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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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.040 | 0.025 |
| Scholarly communication | 0.027 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| 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".