Gallop, Jack: my RCMP United Nations peacekeeping experience (March 9, 2019)
Bibliographic record
Abstract
ABSTRACT: Jack Gallop’s service in the military was apparent in that his answers during the interview were perfectly brief and to the point. Most notable among Gallop’s answers were the details of the interethnic tensions present in Bosnia and Kosovo when he was deployed in 1999. He also discussed the impact his deployment hade upon his family which opened avenues of secondary research regarding the impact deployments can have upon families. Mentions of the presence of UN soldiers, specifically American soldiers were beneficial because a pattern emerged in all 3 interviewees praising the professionalism of the American troops. 0:00 – 10:00 – Background with the police, why Mr. Gallop joined the UN mission, Ottawa orientation, plane trip to the Balkans, local geography and foreign influence in the region, landmines, getting locals to work together. 10:00 – 20:00 – Working with the local police, role of the UN police in Bosnia, ethical practice of the UN with regards to the local population, local tensions in Bosnia, threats to religious minorities in Bosnia, past violence towards Muslims from the local populace. 20:00 – 30:00 – Ancient tensions and vendettas, Mr. Gallop’s transfer to Kosovo, positive changes that Mr. Gallop observed while deployed, presence of foreign UN troops in Kosovo and regions of control, why Mr. Gallop never had to use his sidearm on the mission, issues between different nationalities in the UN police, problems caused by the police. 30:00 – 41:14 – Frustration with the differences between different police practice, excellent conduct of the American police officers, experience of Northern Irish UN officers while working in ethnically tense environments, threat of landmines towards Mr. Gallop, conclusions and reflections from Mr. Gallop. Suggested Clip(s) for Archive: 19:50- 20:44 – More on the tensions between ethnic groups
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".