It’s time for Canadian decisions on lethal drones
Bibliographic record
Abstract
The newly-elected Canadian government of Prime Minister Justin Trudeau is engaged in a defence review process that will result in a new Defence White Paper. There is a strong possibility that Canada acquires more Unmanned Aerial Vehicles(UAVs) for a range of military tasks including disaster relief, surveillance, reconnaissance and the provision of close air support to soldiers in combat. States and sub-state actors can decide to use UAVs or drones to carry out strikes against targets in relatively distant or inaccessible locations. However, this article argues that more consideration, review, transparency and international law regarding drone policy are needed. It suggests Canada take the lead and abide by emerging international law concerning drones as well as become one of the first countries to establish impartisan and unbiased commissions to consider their merits and demerits. Further consideration, monitoring and stringent overview of this relatively new defence technology will be important. It is suggested that Canada’s Department of National Defence declare its intention to take the initiative in the international community in terms of abiding by new and emerging international law, if and when the government decides to acquire significantly more numbers of drones as part of its current defence review process.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".