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Record W2779689168 · doi:10.1111/jssr.12362

Religious Intermarriage in Canada, 1981 to 2011

2017· article· en· W2779689168 on OpenAlexafffundabout
Sharon M. Lee, Feng Hou, Barry Edmonston, Zheng Wu

Bibliographic record

VenueJournal for the Scientific Study of Religion · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsStatistics CanadaUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEndogamyReligious identitySociology of religionSociologyAttendanceIdentity (music)Descriptive statisticsChurch attendanceCensusGender studiesReligiosityEthnic groupDemographyPolitical scienceSocial psychologySocial scienceAnthropologyPsychologyPopulationLaw

Abstract

fetched live from OpenAlex

Abstract Is the social influence of religion weakening as the ranks of the unaffiliated grow and religious attendance falls? Will these changes extend to religion's influence on marriage? This research note contributes to these discussions by examining religious intermarriage as another indicator of religion's social influence. Increased religious intermarriage may indicate a weakening of traditional norms of religious endogamy and a decline in religion's social influence. Descriptive results from examining Canadian census and survey data from 1981 to 2011 show that religious intermarriage increased from 14 percent in 1981 to 19 percent in 2011, but varies by religion and other characteristics. Results from probit models confirm the descriptive findings and further reveal three trends: increasing intermarriage for Protestants, Catholics, Jews, and Buddhists; low and stable intermarriage for Hindus and Sikhs; and decreasing intermarriage for Muslims and the unaffiliated. The findings provide a descriptive base for future research to elaborate and explain the different trends and their implications for religion's social influence on marriage and how this may be changing in societies where religious identity and attendance are also changing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.369
Teacher spread0.330 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations12
Published2017
Admission routes3
Has abstractyes

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