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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.001 | 0.001 |
| 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