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Record W3036488036 · doi:10.1177/1948550620923239

Religious Americans Have Less Positive Attitudes Toward Science, but This Does Not Extend to Other Cultures

2020· article· en· W3036488036 on OpenAlexaff
Jonathon McPhetres, Jonathan Jong, Miron Zuckerman

Bibliographic record

VenueSocial Psychological and Personality Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of Regina
FundersJohn Templeton Foundation
KeywordsReligiosityOddsPsychologySocial psychologySample (material)Logistic regression

Abstract

fetched live from OpenAlex

It is commonly claimed that science and religion are logically and psychologically at odds with one another. However, previous studies have mainly examined American samples; therefore, generalizations about antagonism between religion and science may be unwarranted. We examined the correlation between religiosity and attitudes toward science across 11 studies including representative data from 60 countries ( N = 66,438), nine convenience samples from the United States ( N = 2,160), and a cross-national panel sample from five understudied countries ( N = 1,048). Results show that, within the United States, religiosity is consistently associated with lower interest in science topics and activities and less positive explicit and implicit attitudes toward science. However, this relationship is inconsistent around the world, with positive, negative, and null correlations being observed in various countries. Our findings are inconsistent with the idea that science and religion are necessarily at odds, undermining common theories of scientific advancement undermining religion.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.130
GPT teacher head0.443
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations79
Published2020
Admission routes1
Has abstractyes

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