Frames of law: targeting advice and operational law in the Israeli military
Bibliographic record
Abstract
In this paper I draw on interviews conducted with former Israeli military lawyers about their role in lethal targeting operations. I argue that military lawyers and the practice of operational law help to legitimize and extend violence in the Occupied Palestinian Territories. To make the case I focus on Israel's ‘targeted killing policy’ (2000–present) and on the involvement of military lawyers in the planning and execution stages of targeting operations. I offer two contributions to the literature on war and law; first, I extend Derek Gregory's analysis of the ‘kill chain’ by arguing that targeting is increasingly made possible by a ‘technolegal’ process. Second, I add nuance to Eyal Weizman's account of how law extends violence in what he calls the ‘humanitarian present’. I argue that we must attend not only to international humanitarian law and different scales of law but to the simultaneously plural and overlapping legal regimes that govern late modern war. I conclude with a reflection on Judith Butler's Frames of War to think through the ways in which ‘frames of law’ have come to structure our apprehension of targeting and war today.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".