The Ocular Politics of Targeting: Disembodiment and the Perpetrator Gaze in the War on Terror
Bibliographic record
Abstract
This article analyses the politics of seeing as a way to examine the elision of civilian casualty in the War on Terror. The author particularly focuses on the ambiguities and paradoxes at play in this discussion: the question of distance, the question of visibility and the role of the body. In doing so, she tells the story of how terrorism has emerged as a form of violence that centralizes bodies, focused on the figure of the innocent victim whose body has been destroyed by the body of another, even as the technology of drone strikes also operates by exploding bodies, but through the purported precision of techno-military operations. Such technology re-categorizes civilian death as collateral damage, defining these deaths as technological effects rather than as biological, embodied ones. This acts to disembody dead civilians even as increased attention is being given to soldier bodies (both dead and injured). In this sense, the author is not arguing that civilian death has become disembodied by virtue of the distancing caused by the drone apparatus. Rather, she seeks to tell a more complicated story of how the drone gaze functions as a perpetrator gaze, and who and what it sees.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".