Around the World, Many People Are Leaving Their Childhood Religions
Bibliographic record
Abstract
Pew Research Center conducted this analysis to examine rates of religious switching in 36 countries across the Asia-Pacific region, Europe, Latin America, the Middle East-North Africa region, North America and sub-Saharan Africa. The countries have a variety of historically predominant religions, including Buddhism, Christianity, Hinduism, Islam and Judaism. Among the main findings is that, in many countries around the world, a fifth or more of all adults have left the religious group in which they were raised. Additionally, most of the switching comes from disaffiliation – or people leaving the religion of their childhood and no longer identifying with any religion. For non-U.S. data, this analysis draws on nationally representative surveys of 41,503 adults conducted from Jan. 5 to May 22, 2024. All interviews were conducted over the phone with adults in Canada, France, Germany, Greece, Italy, Japan, Malaysia, the Netherlands, Singapore, South Korea, Spain, Sweden and the United Kingdom. Interviews were conducted face-to-face in Argentina, Bangladesh, Brazil, Chile, Colombia, Ghana, Hungary, India, Indonesia, Israel, Kenya, Mexico, Nigeria, Peru, the Philippines, Poland, South Africa, Sri Lanka, Thailand, Tunisia and Turkey. In Australia, we used a mixed-mode probability-based online panel. For the United States, data comes from the 2023-2024 Religious Landscape Study (RLS). The new RLS was conducted in English and Spanish from July 17, 2023, to March 4, 2024, among a nationally representative sample of 36,908 U.S. adults. Respondents had the option of completing the survey online, on paper, or by calling a toll-free number and completing the survey by telephone with an interviewer. The RLS was made possible by The Pew Charitable Trusts, which received support from the Lilly Endowment Inc., Templeton Religion Trust, The Arthur Vining Davis Foundations and the M.J. Murdock Charitable Trust. This analysis was produced by Pew Research Center as part of the Pew-Templeton Global Religious Futures project, which analyzes religious change and its impact on societies around the world. Funding for the Global Religious Futures project comes from The Pew Charitable Trusts and the John Templeton Foundation (grant 63095). This publication does not necessarily reflect the views of the John Templeton Foundation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".