Bibliographic record
Abstract
Previous research has claimed that world religions can extend the in-group beyond local and ethnic boundaries to form larger multi-ethnic groups, expanding human societies. Two experiments were run in Fiji to test religion’s ability to expand group boundaries. Experiment 1 employed a religious prime to increase prosocial behavior towards co-religionists among Hindu Indo-Fijians in an economic game. There were no overall effects of priming, but gender-specific effects were found. Priming reduced the amount women biased coin allocations to favor their preferred group. Men showed no bias in either condition. Experiment 2 employed the same economic game, without a prime, in a sample of indigenous Fijian and Indo-Fijian Christians. In this game, the monetary allocations were made between different religious and ethnic groups to test if preferences for religious in-groups were stronger than preferences for ethnic in-groups. Indo-Fijian Christians showed bias against their own ethnic group if they were from a different religion (Hindus or Muslims), but allocated fairly towards Christians from a different ethnic group (indigenous Fijians). Indigenous Fijians allocated less money to Muslims, but not Hindus. This evidence suggests that religious bonds can overcome the preference for one’s own ethnic group and expand in-groups to multi-ethnic religious groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".