Bibliographic record
Abstract
This article proposes the concept “extra-lethal violence” to focus analytic attention on the acts of physical, face-to-face violence that transgress shared norms about the proper treatment of persons and bodies. Examples of extra-lethal violence include forcing victims to dance and sing before killing them, souvenir-taking and mutilation. The main puzzle of extra-lethal violence is why it occurs at all given the time and effort it takes to enact such brutalities and the potential repercussions perpetrators risk by doing so. Current approaches cannot account for this puzzle because extra-lethal violence seems to follow a different logic from strategic calculation. To investigate one alternative logic—the logic of display—the article proposes a performative analytic framework. A performative lens focuses attention on the process by which actors stage violence for graphic effect. It highlights the range of roles, participants, and activities that contribute to the production process as a whole. To demonstrate the value of a performative approach, the article applies this framework to three very different extra-lethal episodes: the massacre at My Lai during the Vietnam War, the rape and killing of two women during the Rwandan genocide, and a lynching that took place in rural Maryland. The article concludes by sketching a typology of performance processes and by considering the policy implications of this type of theorizing and knowledge.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".