Book Review: The Structural Prevention of Mass Atrocities: Understanding Risk and Resilience
Bibliographic record
Abstract
Scholarship on the structural prevention of genocide and mass atrocities is, for the most part, saturated with identifying the ‘root causes’ of deadly violence. Conversely, the causes of peace and the processes that de-escalate tensions – in effect, “what goes right” – remain comparatively under researched. In his book The Structural Prevention of Mass Atrocities, Stephen McLoughlin contends that positioning prevention simply on identifying and ameliorating risk factors erroneously assumes a linear inevitability between cause and outcome, and thus “fails to explain why some at-risk countries experience mass atrocities, yet others do not” (3). McLoughlin convincingly advocates an analytical framework, which broadens structural prevention to include local and national conditions that mitigate risk by fostering resilience and stability. He then applies this framework to the cases of Botswana, Zambia, Tanzania and (an internal, relatively autonomous area within Tanzania), Zanzibar. By introducing a model that navigates the complex relationship between risk and resilience, McLoughlin complements the chorus of scholars asking “why?” mass atrocities occur, by asking “why not?” This book gently reminds readers that there are invaluable lessons to be learnt from peaceful non-events as much as from international tragedies.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".