The Liminal Figure of the Civilian and the Necropolitics of Protection
Bibliographic record
Abstract
Abstract Recent literature on contemporary humanitarian governance brings to light complex rationales of care and control that give expression to the ongoing coloniality of power. This paper examines the protection of civilians (PoC) from a similar vantage point. Over the past few decades, the goal of protecting civilians has produced a broad assemblage meant to guide military, police, and humanitarian operations in conflict environments. This paper argues that to “protect civilians” is to rationalize human life along a narrow biopolitical continuum grounded in the liminal figure of the civilian, a figure that in war can appear alive or dead. While the goal of protection is evidently to keep civilians alive, the rationality at work in the PoC assemblage has also given way to a particular form of necropolitics that enmeshes life and death. Following a familiar colonial profile, but one that functions to obscure racialization, the necropolitics at work in PoC begin with a quantificatory episteme of accounting for, and a counting of, civilian casualties. This has led to the establishment of civilian casualty tracking and mitigation cells as a model meant to generate lessons learned from civilian casualties. At work in the PoC, therefore, is a political theory of life that enmeshes a form of biopolitics and necropolitics: a politics of life and a politics of death.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.074 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".