Bibliographic record
Abstract
Abstract Why do some civil wars feature the mass killing of civilians while others do not? Recent research answers this question by adopting a ‘varieties of civil war’ approach that distinguishes between guerrilla and conventional civil wars. One particularly influential claim is that guerrilla wars feature more civilian victimization because mass killing is an attractive strategy for states attempting to eliminate the civilian support base of an insurgency. In this article, I suggest that there are two reasons to question this ‘draining the sea’ argument. First, the logic of ‘hearts and minds’ during guerrilla wars implies that protecting civilians – not killing them – is the key to success during counterinsurgency. Second, unpacking the nature of fighting in conventional wars gives compelling reasons to think that they could be particularly deadly for civilians caught in the war’s path. After deriving competing predictions on the relationship between civil war type and mass killing, I offer an empirical test by pairing a recently released dataset on the ‘technology of rebellion’ featured in civil wars with a more nuanced dataset of mass killing than those used in several previous studies. Contrary to the conventional wisdom, I find that mass killing onset is more likely to occur during conventional wars than during guerrilla wars.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".