Religiously Traditional, Unusually Supportive? Examining Who Gives, Helps, and Advises in Americans’ Close Networks
Bibliographic record
Abstract
A large literature is currently contesting the impact of religion on prosocial behavior. As a window into this discussion, I examine the close social networks of American adults and consider whether religious traditionalists are more likely than other network members to supply several basic forms of social support. Analysis of the Portraits of American Life Survey reveals three main findings. First, a majority of Americans—religious or not—count at least one perceived religious traditionalist among their close network ties. Second, American adults are more likely to receive advice, practical help, and money from ties identified as religious traditionalists than from other types of ties, a pattern that held among both kin and nonkin network ties. Finally, although perceived traditionalist network members appear especially inclined to assist highly religious people, they nevertheless offer social support to Americans across a broad spectrum of religiosity. Beyond its relevance for debates on religion and community life, this study also proposes a novel strategy to assess prosocial behavior. Asking people to recount the deeds of their network members can reduce certain self-reporting biases common to survey research and helps locate prosocial activity in concrete and meaningful social relationships.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".