Targeted Killings in International Law: Considering Extra-judicial Killings on Canadians Abroad
Bibliographic record
Abstract
The use of targeted killings has become more typical since the US declaration of a “Global War on Terror”. States such as the US and Israel have employed targeted killings as a means to combat the growing threat of international Islamic terrorism; the US has transitioned from a law enforcement paradigm to a law of war paradigm, through the Congress’ Authorization on the Use of Military Force. Although the legality of targeted killings is still contested in the international community, I argue that while the law enforcement paradigm is ineffective at containing the growing threat of terrorism, the law of war paradigm disregards international law and risks the protections of civilians unnecessarily. More constraints are needed through international law in order to maintain the core principles of the international humanitarian framework, while still combating terrorism and expanding the existing framework to cover non international armed conflicts such as that between al-Qaeda and the US. This can be done through the establishment of a new paradigm, called the continuous hostilities paradigm. If the existing international principles such as distinction, proportionality, military necessity and humanity are considered, targeted killings can be legal under international law. However, the indiscriminate killing of suspected terrorists by States cannot be considered legal, and it is crucial to consider the necessity of the protection of civilians
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".