Bibliographic record
Abstract
In most jurisdictions, any claim to emergency power must be necessary. But necessity is an ambiguous concept which has historically facilitated abuse. Indeed in the Canadian context, though necessity is a key threshold requirement in the Emergencies Act, it remains a black box, constituting a worrying gap in that Act’s robust framework for accountability and oversight. This paper develops a set of heuristic tools to clarify and rigorously assess claims of necessity, providing a structure for a government’s reason-giving around emergency declarations and measures. Because such reason-giving is critical to the rule of law in a state of emergency I conclude by advocating, in the Canadian context, an amendment to section 61 of the Act: When Government tables measures with Parliament or the Parliamentary Review Committee, they should explicitly state why an order or measure is necessary.
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 | 0.046 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.065 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".