A surprising spat: The causes and consequences of the Saudi–Canadian dispute
Bibliographic record
Abstract
In August 2018, Saudi Arabia expelled the Canadian ambassador to Riyadh, recalled its own ambassador in Ottawa, and imposed sanctions on Canada. This overreaction to tweets by the Canadian foreign ministry demanding the release of jailed activists was consistent with the pattern of foreign policy assertiveness that has accompanied the rise of Mohammed bin Salman (MbS). The Saudi–Canadian spat is not very important: bilateral ties were never essential for either country. But the dispute carries lessons for Canada and its allies as they reflect on future ties to the Kingdom. This partnership was always necessary but costly. MbS, however, has amplified and exposed these costs. By bringing unprecedented scrutiny to Saudi actions, recent events have opened a window of opportunity for Canada and its allies to re-evaluate relations. They should increase pressure on Riyadh to change the costliest aspects of its policies. Should this fail, they should downgrade the partnership.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.037 | 0.015 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".