Bibliographic record
Abstract
Canada’s emergency legislation, the Emergencies Act, was examined as part of the Public Inquiry into the 2022 Public Order Emergency. This Inquiry recommends several amendments to the Emergencies Act but does so without examining the wider context of Canada’s emergency management system. This paper looks at that context to explain why the only legislative tool available to respond to the 2022 protests was, at best, adequate. The underlying assumption is that Canada has an effective and efficient emergency management system that is only hampered by out-of-date legislation. Examining the historical development shows that the Emergencies Act was drafted in the absence of a robust emergency management system that subsequently evolved in ways that make the legislation further out of step. Amendments to the Emergencies Act must resolve the current overlap with matters of provincial concern that can currently arise during a national emergency for public welfare and public order. Federak emergency powers should only deal with ways the federal government is likely to get involved within its own jurisdictional powers and in light of current federal legislation.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".