Missing pieces and body parts: On bodily integrity and political violence
Bibliographic record
Abstract
While much attention is paid to what happens to dead bodies after political violence, disaster, or atrocity, less attention has been paid to body parts, despite the wide-ranging efforts, both material (often forensic) and discursive, to reconstitute or resuscitate the whole dead body. Materializing the whole body is often considered key to truth-telling mechanisms and for closure for family members of the missing and dead, thus the body part is often posited as a problem in need of a solution. We are seeing, largely due to advances in forensic technologies, an increasing belief that all body parts can be identified and distinguished from other materials, and should, therefore, be recovered and repatriated to the whole body in its death. To explore this dynamic, I make two key arguments. First, I suggest that reassembling bodies is framed as a mechanism of re-subjectification that is key to reconciliation and justice after political violence. A body part is an object, but a dead body is in most contexts still considered a subject, even dead, so putting a dead body back together is considered re-humanizing and gives the dead body back its political agency. Second, I suggest that when this cannot be done materially due to the obstacles posed by modern warfare, we often see governance techniques that seek to do so discursively.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.135 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".