Beyond the erotics of Orientalism: Lawfare, torture and the racial–sexual grammars of legitimate suffering
Bibliographic record
Abstract
Abstract Contrary to commonsense understandings of torture as a form of information-gathering, confessions elicited through the use of torture produce notoriously unreliable data, and most interrogation experts oppose it as a result. With a focus on the US carceral regime in the War on Terror, this article explores the social relations and structures of feelings that make torture and other seemingly ineffective and absurd carceral practices possible and desirable as technologies of security. While much of international relations scholarship has focused on the ways in which affective and material economies of Orientalism are central to representations of the ‘terrorist’ threat, this article connects the carceral violences in the racialized lawfare against Muslimified people and spaces to the capture and enslavement of Africans and the concomitant production of the figure of the Black body as the site of enslaveability and openness to gratuitous violence. The article further explores how these carceral security practices are not simply rooted in racial–sexual logics of Blackness, but themselves constitute key sites and technologies of gendered and sexualized race-making in this era of ‘post-racial triumph’ (HoSang and LaBennett, 2012: 5).
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.109 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".