Modern Finance-Centric Governance: the 2022 Emergency Measures, Property and Financial Powers
Bibliographic record
Abstract
A distinctively financial hammer was used to shutter the February 2022 public order emergency. Bank accounts were frozen, donation conduits were squeezed, financial intermediaries were placed under surveillance, and protestors and their financial supporters were met with the risk of severe sanctions. While this tethering of finance and property to end the 2022 Convoy uprising elicits a certain surprise, it is also familiar territory: it sits well within modern crime control policy. The greater surprise is not the type of hammer but the finding of this tool within the box of federal emergency powers that might be leveraged to deal with a public order emergency. This brief note examines the distinct finance and property related measures used in 2022 and their relationship to the federal Emergencies Act. It recommends that Parliament engage in careful deliberation over the appropriateness of financial and property measures for responding to a public order emergency, that the permissibility of such measures be clearly specified and constrained by the statutory language of s. 19(1) and that s. 19(1) be further amended to explicitly require review for Charter compliance of all emergency measures prior to any implementation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".