Transforming men into killers: Attitudes leading to hands-on violence during the 1994 Rwandan genocide
Bibliographic record
Abstract
Background: Although public health-based violence prevention trials have been successful in a variety of high-risk settings, no study has addressed the prevention of genocide, a form of population-based, catastrophic violence. Objective: To develop a model, including a panel of modifiable attitudinal patterns, explaining why civilians assaulted or murdered targeted victims during the 1994 Rwandan genocide. Design, setting, and participants: A grounded theory inquiry consisting of interviews with 41 Rwandan genocide perpetrators in six Rwandan provinces in 2005. Results: Respondents described two distinct environmental contexts (life prior to April 1994 and life during the 1994 genocide) that informed beliefs, decisions, and behaviour. In addition, all respondents described four experiential pressures shaping their choices to participate in the genocide: defending home and nation; fear of governing authorities; greed; and feeling overpowered, confused, or ambivalent. For the sub-sample of respondents under 21 years old, a fifth experiential pressure, transitions from adolescence to adulthood, was also described. A unique combination of these factors motivated each individual's behaviour, and shifted and evolved with new situations. Conclusions: A complex interaction between self and national defence, fear of ruling authorities, and overpowering social upheaval exerted pressure on average citizens to perpetrate hands-on violence during the 1994 Rwandan genocide. These findings may have future implications for understanding and preventing catastrophic violence in other high-risk jurisdictions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".