Ethical interventions: Non-lethal weapons and the governance of insecurity
Bibliographic record
Abstract
Abstract This article employs some of the theoretical and methodological tools devised by Michel Foucault to explore the political rationale suggested by the proliferation and use of a class of weapons collectively referred to as ‘non-lethal’. The invention and continued use of non-lethal weapons has been treated in existing literature as an ethical crisis. This article connects the emergence of non-lethal weaponry to the mobilization of a sense of ethical crisis concerning the humane treatment of civilians and combatants in conflicts in the United States and beyond. Policies related to non-lethal weaponry, along with the practices that they engender, are also explored in relation to the notion of ‘partial citizenship’. Offering a contribution to the genealogy of non-lethal weapons, this article traces their involvement in the policing by US military agents of a variety of sites, actors, and contexts outside of the theater of war.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.074 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".