A review of Canada’s use of autonomous sanctions under the Special Economic Measures Act (SEMA) and the Justice for Victims of Corrupt Foreign Officials Act (JVCFOA) between 2017-2021
Bibliographic record
Abstract
The use of autonomous sanctions by Canada and its allies has increased significantly over the last 30 years, yet there is little research that examines how Canada uses these measures and in what circumstances. This thesis asks in what circumstances does Canada resort to using autonomous sanctions measures, and documents how Canada has used the Special Economic Measures Act (SEMA) and the Justice for Victims of Corrupt Foreign Officials Act (JVCFOA)— Canada’s Magnitsky legislation— between 2017-2021. This time scale was chosen because the JVCFOA was adopted in 2017, and at the same time, the SEMA legislation was updated to expand the circumstances in which it can be invoked. Notably, there is a legislated requirement for the committees of the Senate and of the House of Commons that are designated or established by each House to review both pieces of legislation before October of 2022, and the JVCFOA has remained unused in over three years (since November 2018). This research finds that Canada is not using its legislation in a coherent manner, which is exacerbated by a lack of transparency by the Government of Canada in terms of how decisions are made regarding who it targets with sanctions and why. This thesis concludes with policy-relevant recommendations made in three categories: changes to the SEMA and JVCFOA legislation, administrative and legal, and outreach, education, and communication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".