Persecuted Minorities and Defensive Cooperation: Contributions to Public Goods by Hindus and Muslims in Delhi
Why this work is in the frame
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Bibliographic record
Abstract
How does intergroup inequality, specifically minority experiences of persecution, affect contributions to local public goods? Based on an original survey experiment and qualitative research in slums in Delhi, we examine how Hindus and Muslims respond to social norms around promoting cooperation on community sanitation. Mainstream theories of development predict greater willingness to contribute to public goods in more homogeneous areas. In contrast to the “diversity-deficit hypothesis,” however, we find that social accountability mechanisms are more effective among Muslims, a group that routinely faces discrimination and violence in India. We propose that this reflects “defensive cooperation,” or a set of coping strategies developed by minorities to navigate a hostile sociopolitical environment. Our findings point to a new mechanism that helps to enforce prosocial norms and, hence, public goods provision in multiethnic contexts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it