Canada's 'Unilateral' Sanctions Regime under Review: Extraterritoriality, Human Rights, Due Process, and Enforcement in Canada's Special Economic Measures Act
Bibliographic record
Abstract
In the Fall of 2016, the House of Commons Standing Committee on Foreign Affairs and International Development began a critical review of Canada’s “unilateral” sanctions legislation, the Special Economic Measures Act (SEMA), which culminated in the Justice for Victims of Corrupt Foreign Officials Act (Sergei Magnitsky Law). This resulted in the first legislative amendment to SEMA since 1992 when the legislation was first passed. While the impetus for the legislation re-evaluation was rightfully the debate around the so-called Sergei Magnitsky Laws — amendments that allow for the sanctioning of human rights abusers abroad particularly in Russia — Parliament should now go much further with regards to SEMA. It is time not only to amend SEMA to clarify that sanctions can be promulgated where gross human rights abuses are taking place abroad, but also to allow for “double” extraterritorial sanctions against covert “sanctions-busters.” That is, future legislative amendments should take account of modern business practice, recognize that Canada is a hub for sanctions-bust-ing, and close the loophole that currently exists in SEMA, preventing Canada from targeting companies in non-sanctioned countries in the business of transshipping goods between Canada and sanctioned countries. Moreover, the Parliamentary attention to economic sanctions is an opportunity to take a broader look at SEMA and particularly the practice of enacting and enforcing new sanctions. This paper recommends a sanctions coordination unit to ensure proper interdepartmental security cooperation on the SEMA sanctions file, coupled with proper governmental oversight or review, and statutorily- mandated review of all sanctions listings every two years. It is time to bring SEMA into the twenty-first century, and with it, government practice implementing and enforcing the legislation. This will require some changes to the legislation, but also a myriad of changes to government practice on the file.
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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 | 0.026 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.022 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.018 | 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".