Democidal Thinking: Patterns in the Mindset Behind Organized Mass Killing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We derived a model identifying observable attitudes among perpetrators of democides - mass-killing programs associated with governments that cost over 160 million lives in the last century. These attitudes, evident in rhetoric mobilizing support for killing, have previously received too little systematic study. Content-analysis of text from 20 prominent, diversely sampled cases of democide from around the world yielded 20 typical features of democidal mindset, present in most cases. These prominently included essentialist beliefs in out-group inferiority, dehumanization and moral exclusion, a paranoid-thinking style, and certain forms of nationalism, among numerous other features. These can function to facilitate the inculpation of an out-group and fuel eliminationism. The joint operation of multiple such themes in a web of violence-conducive beliefs appears to generate hazard, not any single isolated theme. With further model refinement, identification of sufficient density of themes in rhetoric might promote peace by providing early-warning indicators of mass violence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it