Theorizing Destruction: Reflections on the State of Comparative Genocide Theory
Bibliographic record
Abstract
This article reviews the current state of comparative genocide theorizing, focusing on theories that attempt to account for the causes of genocide and the processes of genocidal killing.The literature is divided into three broad categories, based on the relative weight given to (a) individual or group agency, (b) structural factors, or (c) processes of identity construction in accounting for the origins and unfolding of genocidal destruction.The discussion of agencyoriented approaches focuses on theories that suggest that genocide is driven, in terms of decision making and perpetration, by elite decision makers, front-line perpetrators, and societal behavior.The literature on structural approaches is broken down into theories that stress the importance of culture, institutional organizations, societal cleavages, structural crises, regime type, modernity, and ideology.The final section reviews the literature on processes of collective identity construction.The article suggests throughout and in conclusion that although comparative genocide theorizing has come a long way in proposing a number of different explanations for the onset of genocide and the nature of genocidal processes, more work needs to be done with respect to the precise operationalization and testing of theories according to more rigorous comparative methodological practices.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.106 |
| Scholarly communication | 0.011 | 0.028 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".