Neuroscience and Non-Lethal Violence in Genocide: Exploring Scope and Constraints
Bibliographic record
Abstract
Beyond findings in psychiatry and psychology, in the last two decades, the novel field of neuroscience has expanded our purview of brain injury, pain, and trauma—basically, non-lethal forms of violence. Peeking into the brain using state-of-the-art neuroimaging enables us to discern anomalies such as brain lesions, cysts, enlarged sulci, and regions of hypoperfusion (reduced blood flow), among other things, thereby allowing us to infer connections between brain anomalies and behavior. These findings, this paper posits, are highly relevant to genocide scholarship. Scholars have suggested that “mental harm” in Article II(b) of the Genocide Convention be extended to include neuroscientific findings; the most common in the literature being Post-traumatic Stress Disorder (PTSD), Traumatic Brain Injury (TBI), and Chronic Traumatic Encephalopathy (CTE). Including these in determining short- and long-term, visible and non-visible effects of exposure to mass atrocity and genocide would allow for a more accurate assessment of non-lethal violence on survivors and their families. Moreover, sufferers would be in a stronger position to employ restorative justice comprising legal, medical, and pecuniary means. Despite the excitement over these revelations, a question to ponder are the challenges individuals and institutions would face before they incorporate non-lethal violence derived from neuroscientific findings into genocide scholarship. This paper explores practical, technical, ideological, and legal arguments: (1) neurohype, (2) limitations of neuroimaging, (3) ideological battles, (4) gatekeeping the definition of genocide.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.064 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".