Canada and the United Nations: Setting the Record Straight
Bibliographic record
Abstract
In response to the 2023 report, “Canada and the United Nations: Rethinking and Rebuilding Canada's Global Role,” Jack Cunningham argues that Canada should instead marginalize the United Nations. In response, this rejoinder maintains that, while the UN has weaknesses, the world would be a worse place without it. Look to the UN's work to close the hole in the ozone layer, deliver life-saving vaccines to millions, or give food and shelter to the displaced. The UN has been a remarkably successful norm-setter, creating vital international standards on issues that range from the more political to the technical. Cunningham favours a retreat by Canada to smaller clubs of like-minded states, without explaining how this retrenchment might produce more effective results on global issues, such as climate change. He offers no alternative to the UN. It is hard to see how abandoning the field to China, Russia, and Iran, would serve Canada's interests.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.031 | 0.018 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".