The Boundaries of Trust: Cross-Religious and Cross-Ethnic Field Experiments in Mauritius
Bibliographic record
Abstract
Several prominent evolutionary theories contend that religion was critical to the emergence of large-scale societies and encourages cooperation in contemporary complex groups. These theories argue that religious systems provide a reliable mechanism for finding trustworthy anonymous individuals under conditions of risk. In support, studies find that people displaying cues of religious identity are more likely to be trusted by anonymous coreligionists. However, recent research has found that displays of religious commitment can increase trust across religious divides. These findings are puzzling from the perspective that religion emerges to regulate coalitions. To date, these issues have not been investigated outside of American undergraduate samples nor have studies considered how religious identities interact with other essential group-membership signals, such as ancestry, to affect intergroup trust. Here, we address these issues and compare religious identity, ancestry, and trust among and between Christians and Hindus living in Mauritius. Ninety-seven participants rated the trustworthiness of faces, and in a modified trust game distributed money among these faces, which varied according to religious and ethnic identity. In contrast to previous research, we find that markers of religious identity increase monetary investments only among in-group members and not across religious divides. Moreover, out-group religious markers on faces of in-group ancestry decrease reported trustworthiness. These findings run counter to recent studies collected in the United States and suggest that local socioecologies influence the relationships between religion and trust. We conclude with suggestions for future research and a discussion of the challenges of conducting field experiments with remote populations.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".