Victims or Madmen? The Diagnostic Competition over “Terrorist” Detainees at Guantánamo Bay
Bibliographic record
Abstract
This article traces the pathologization of suspected terrorists held captive at the U.S. military base in Guantánamo Bay, Cuba. The occurrence of several reported suicide attempts among the detainees provided “proof” for their captors that terrorists are indeed fanatical madmen. These same reports of suicide attempts, however, were contradictorily diagnosed by human rights and humanitarian organizations as evidence of psychological deterioration induced by prolonged detention. What is notable in this diagnostic competition over what, exactly, afflicts the detainees is that both advocates and resisters to the detentions pathologized the detainees by chalking up the suicide attempts to their purportedly impaired psyches. This is significant because the pathologization of the detainees is one condition of possibility for their excision from the global body politic. Authoritarian practices, such as the incarceration of the “mad” and the detention of “suspected terrorists,” are theorized as integral to global liberal governance, which divides up populations, and subjects those deemed mad, deviant, or dangerous to coercive measures in the name of order, security, and liberty. The article concludes with a consideration of the ways in which international relations (IR) could be more attentive to the operation of the “psy” disciplines in the conduct of international affairs.
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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 | 0.010 | 0.018 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".