Bibliographic record
Abstract
Although Canada had been a keen supporter of, and a contributor to, the United Nations' early peacekeeping operations, she was at first most reluctant to get involved in the Force which the Security Council decided in March 1964 should be sent to Cyprus. Then, overnight, she became a zealous advocate of participation. These disparate responses seem strange. It is suggested that the explanation for her reluctance lies chiefly in the changing political scene in Canada, which made her anxious to distance herself somewhat from Britain--who was urgently seeking peacekeeping help in Cyprus. As to the second part of this mystery, it is argued that the explanation lies in the decision of the External Affairs Minister, Paul Martin, to put his personal imprint on the situation. A further mystery lies in the fact that the first Canadian contingent sent to Cyprus favoured a tougher approach to its role than the UN's Force Headquarters deemed appropriate for a peacekeeping body. The writer has no explanation for the contingent having taken that line. As to the 'mistake', it is argued that this lies in the widespread belief that Canada--and Martin in particular--was chiefly responsible for breaking the logjam which was holding up the creation of the Force. Canada did indeed accelerate its creation by a day. But it is claimed that the credit for getting the Force on the ground lies primarily with Finland, and secondarily with Sweden.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".