The Search for Order: Understanding Hindu-Muslim Violence in Post-Partition India
Bibliographic record
Abstract
One distinguishing feature of mainstream social science is its growing regard for model building and formal hypothesis testing. In South Asian studies this is most evident in accounts of ethnic riots or communal violence. This paper examines a model of votes and violence proposed by Steven Wilkinson. We first examine how well the model performs against a data set that we have assembled on the twenty worst incidents of communal violence in India since 1950. The Wilkinson model is consistent with some important key facts in our data set, most notably in terms of levels of urbanization and "percentage Muslims" in riotaffected towns and cities. However, proximity to national or state elections is not found to be a strong driver of prolonged ethnic rioting. Nor is it the case that India's worst instances of communal violence occurred mainly where there was direct electoral competition between less than 3.5 effective political parties, the other main predictive variable in the Wilkinson model. We then discuss the limitations more broadly of attempts to explain and even predict ethnic violence within the framework of a quantitative model. We pay attention to time inconsistencies, principal-agent problems, religiosity and the homogenization of riot events, and omitted variables (notably, memory work and ideological fervour). We conclude with some general remarks on the search for order in social science.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".