Bibliographic record
Abstract
All cases of genocide in the modern era feature counterinsurgency in some capacity. Often, genocidal acts are justified as counterinsurgency, and counterinsurgency doctrines and tactics are employed to carry out many genocides. While genocides often have international dimensions, they are mostly carried out within the context of intrastate armed conflicts, almost all of which can be characterized as counterinsurgency. In this article, I expand upon Martin Shaw's model of Genocide as War by exploring the theoretical linkages between counterinsurgency and genocide to demonstrate where counterinsurgency fits into the genocide process. Two specific linkages are drawn to show how counterinsurgency complements the genocide process: total transformation of society through militarization, and exploitation of the asymmetries of power between the opposing groups. The relationship between counterinsurgency and genocide is not constructed as a causal one, but recursive (i.e., mutually reinforcing). By examining the Rwandan and Guatemalan Genocides, I demonstrate how genocide is operationalized through counterinsurgency in both cases. I conclude by providing areas for further investigation toward a unifying theory between the scholarships on genocide and counterinsurgency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".