Bibliographic record
Abstract
The Canadian government announced in June 2017 that it was planning to purchase and deploy armed drones. Yet to date it has provided virtually no information on how and for what purpose such armed drones would be used, beyond anodyne comments that they would be used like any other conventional weapon. However, conventional weapons have varying capabilities and purposes, and implicate international law in different ways as a result. Armed drones have been primarily used for the purpose of targeted killing, in ways that have raised significant legal questions and triggered claims of excessive civilian deaths. Canadians should be concerned about how, for what purpose, and according to what limitations the government plans to deploy armed drones. Other countries have provided greater transparency than Canada regarding the law and policy framework governing the use of armed drones. This short essay reviews how armed drones have been used elsewhere, explains the significant legal issues that are implicated by the different ways in which drones have been used and what that implies for the role of Canadian armed drones. It suggests that strict, clear and publicly disclosed limits be placed on drone use to ensure compliance with Canada’s international law obligations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".