Bibliographic record
Abstract
The nature of religious change and the future of religion have been central questions of social science since its inception. But empirical research on this question has been quite American-centric, encouraged by the conventional wisdom that the United States is an outlier of religiosity in the developed world, and, more pragmatically, by the availability of survey data. The dramatic growth in the number and reach of cross-national surveys over the past two decades has offered a corrective. These data have allowed research on religious trends in the United States, Canada, and Europe, putting American trends into comparative relief. This research synthesis reviews the past quarter century of cross-national comparative survey research on religious behavior, focusing on religious service attendance as a commonly measured behavior that is arguably more equivalent across societies and cultures than other measures of religiosity. The lack of evidence for religious revival is highlighted, noting instead declining rates of attendance in the United States and Canada, and either declining rates or low "bottomed-out" stability in Western Europe, most of Eastern Europe, and Australia and New Zealand. Finally, countries in Latin America, Africa, and Asia are discussed to the extent that research allows, before a call for future research-in these places in particular-is made in order to correct for the Western and Christian focus of much of the research on cross-national religious trends.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".