Cigarettes for the dead: effects of sorcery beliefs on parochial prosociality in Mauritius
Bibliographic record
Abstract
Research testing evolutionary models of religious morality shows that supernatural beliefs in moralizing gods positively affect prosociality. However, the effects of beliefs related to local supernatural agents have not been extensively explored. Drawing from a Mauritian Hindu sample, we investigated the effects of beliefs and practices related to two different types of local supernatural agents (spirits of the deceased unconcerned with morality) on preferential resources allocation to receivers differing in geographical and social closeness to participants. These spirits are ambiguously linked to either ancestor worship or sorcery practice. Previous studies suggested that sorcery beliefs erode social bonds and trust, but such research is often limited by social stigma and missing relevant comparison with other beliefs. To overcome these limitations, we used nuanced free-list data to discriminate between the two modes of spirit beliefs and tested how each contributes to decision-making in economic games (Random Allocation, Dictator). Expressing sorcery beliefs together with performing rituals addressed to the spirits was associated with greater probability of rule-breaking for selfish/parochial outcomes in the Random Allocation Game (compared to ancestor worship). No difference in money allocations was found in the Dictator Game.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".