Symbolic act, real consequences: Passing Canada’s Magnitsky Law to combat human rights violations and corruption
Bibliographic record
Abstract
Both the volume of economic sanctions and the reasons for their imposition have increased tremendously around the globe. In this context, several countries, including the United States and Canada, have introduced Magnitsky acts to enable their governments to act unilaterally to impose sanctions against foreign actors for gross violations of human rights and significant acts of corruption. This paper evaluates the legislative changes made to Canada’s sanction regime in 2016–2017 and explores how the new authorities have been applied following implementation (2017–2019). We find that, despite granting the Canadian government new authorities to undertake autonomous sanctions, the country has continued to cooperate with other states as it had done prior to the changes. We conclude that lawmakers never intended for Canada to use the new autonomous capabilities to “go it alone.” Instead, the symbolism represented by Canada taking a strong stance against human rights abuses globally was the driving force behind the Magnitsky Law’s passage.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".