#StopThisMovie and the Pitfalls of Mass Atrocity Prevention: Framing of Violence and Anticipation of Escalation in Burundi’s Crisis (2015-2017)
Bibliographic record
Abstract
The ongoing Burundi crisis offers a unique opportunity to scrutinize the changing political economy of preventive framing of violence, and particularly genocide as a representational resource in prevention. The paper shows that labels and labeling practices are not disconnected from the local dynamics of conflict, and might have counterintuitive effects in this respect. The portrayal of Burundi’s crisis— the frequent intimations that the recent crisis can lead to genocide, the invocations of the ethnic frame, and the repeated comparisons with Rwanda and Burundi’s own past— has proceeded through a problematic analysis-by-analogy and has served to obscure the core drivers of the recent violence and the dynamics of escalation on the ground. Further to this, the portrayal has not only proven ineffective in translating increased attention into action, it also has had three unintended and potentially perverse effects on the conflict itself, together fueling the political standoff rather than helping to resolve the crisis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".