Canada’s sanctions regimes: an investigation into Canada’s use of sanctions between 1990 and 2014.
Bibliographic record
Abstract
This thesis asks a simple question: what is the state of Canada’s sanctions practice since 1990? In a post-Cold War environment, sanctions have become one of the most commonly applied tools of statecraft. Sanctions are commonly applied to address all manner of crises be they interstate aggression, intrastate humanitarian crises, civil wars, illegal seizures of power, arms proliferation, and international terrorism. There has been no sustained analysis of Canada’s use of sanctions since Kim Richard Nossal’s book Rain Dancing, which only investigated Canada’s application of sanctions in comparison to Australia’s until 1990. Therefore, there is a significant gap in the general sanctions literature and, more worrisome, Canada’s foreign policy literature. This thesis conducts an investigation into Canada’s use of sanctions since 1990 to establish when, why and with whom Canada has applied economic sanctions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".