Horizontal Economic Inequality and Mass Atrocity Risk: A Large-Sample Empirical Inquiry
Bibliographic record
Abstract
Our research question is: Does inter-group horizontal economic inequality elevate state-perpetrated mass atrocity risk? Theoretical perspectives in genocide studies show how economic and other forms of discrimination against ethnic or religious groups can elevate the risk of government violence against them. Among the approximately five dozen large-sample empirical studies of mass atrocity risk, only a few consider the effects of economic discrimination. Moreover, no large-sample empirical studies, to the best of our knowledge, test hypotheses related to how inter-group horizontal economic inequalities (as distinct from vertical economic inequalities based on GINI coefficients or quantile income or wealth measures) affect mass atrocity risk. Drawing upon two data sources, we construct four horizontal economic inequality measures for groups within nations. The measures relate to access to economic resources in general and to electricity in particular. We then empirically test hypotheses related to horizontal economic inequality and mass atrocity risk for a sample of 175 nations spanning the period 1946–2019. We find modest support that horizontal economic inequality elevates mass atrocity risk broadly defined, but not genocide risk. We also find that vertical income inequality measures do not usually elevate mass atrocity or genocide risk.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".