Snow, Bud: my RCMP United Nations peacekeeping experience (March 12, 2019)
Bibliographic record
Abstract
ABSTRACT: Bud Snow provided probably the longest and most detailed interview on account of his pleasantly informative tangents that revealed more than I had prepared for. He revealed how the American military proved to be great allies of the police and allowed them to use their facilities. Smaller details about his interactions with the local populace were even better such as his telling of how the UN police were guests at local weddings. Other information included 0:00-10:00 – Motives for joining police, Joining the UN mission, Preparing in Ottawa. 10:00 -20:00 – Working with foreign police officers, The “Adventure” for going overseas, UN buildup in Macedonia (staging area), role played by Snow. 20:00 – 30:00 – Division of Kosovo among different UN-affiliated countries, description of Camp Bonsteel (US Base), relationship of the police with the UN military, “naivety” of the officers deployed. 30:00 – 40:00 – Communications between different nationalities (English proficiency), dangers of locals working for the UN, ethnic makeup and tensions of assigned regions, murders committed by locals during the mission. 40:00-50:00 – History of hatred between ethnic groups in Kosovo, growth of local confidence and trust in the UN police compared to the military, threat of landmines, 50:00 – 1:00:00 – Danger to Albanians when travelling through Serb areas, US military attached to UN save the police when they came under fire. 1:00:00 – 1:10:00 – Working with non-Canadian UN officers, differences in conduct between different nationalities, police vs military relations with local people, assertion of the success of the mission by Mr. Snow. 1:10:10-1:20:00 – Shared hatred of the Roma by Albanians and Serbs, most violent locations to work in Kosovo, firepower of the Americans at Camp Bondsteel. 1:20:00 – 1:27:58 – Accessing alcohol on UN bases, local cuisine enjoyed by the UN police officers, Mr. Snow’s later career with the UN, conclusion. Suggested Clip(s) for Archive: 34:20 – 38:05 – Local tensions in the area including how it affected the safety of the UN’s interpreters
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".