The electoral consequences of mass religious events: India's Kumbh Mela
Bibliographic record
Abstract
Abstract Mass ritualized gatherings like pilgrimages are central to religious practice globally. Do they generate votes for religious parties? The events may heighten religiosity, enlarging support for parties seen as owning religious policy issues. Such parties might also co‐opt the events to organize and campaign. We evaluate the electoral impact of India's Kumbh Mela, a Hindu festival considered the world's biggest human assembly, leveraging its astrologically determined timing combined with districts’ proximity by rail to the festival sites. The Kumbh Mela boosts Hindu nationalists’ vote share. Mechanisms tests suggest it does so by increasing religious orthodoxy—seen in the adoption of Brahminical dietary practices—and by strengthening party capacity. There are mixed effects on communal conflict. The events are electorally polarizing; they cause India's main secular‐leaning party to perform better in regions with denser concentrations of religious minorities. Our study offers a new account of how confessional parties make inroads in multiethnic democracies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".