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Record W2052744196 · doi:10.1007/s11266-013-9428-8

Religious and Secular Voluntary Participation by Immigrants in Canada: How Trust and Social Networks Affect Decision to Participate

2013· article· en· W2052744196 on OpenAlexfundaboutno aff
Lili Wang, Femida Handy

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsImmigrationAffect (linguistics)AttendancePrideVoluntary associationReligious organizationSocial integrationTurnoverGeneral Social SurveyChurch attendanceDispositionMarital statusSociologySocial psychologyDemographic economicsPolitical sciencePsychologyDemographyLawPopulationReligiosity

Abstract

fetched live from OpenAlex

Abstract Participation in voluntary associations is an important part of an immigrant’s integration into a host country. This study examines factors that predispose an immigrant’s voluntary involvement in religious and secular organizations compared to non-immigrants (“natives”) in Canada, and how immigrants differ from natives in their voluntary participation. The study results indicate that informal social networks, religious attendance, and level of education positively correlate with the propensity of both immigrants and natives to participate and volunteer in religious and secular organizations. Immigrants who have diverse bridging social networks, speak French and/or English at home, and either attend school or are retired are more likely to participate and volunteer for secular organizations. Further, social trust matters to native Canadians in their decision to engage in religious and secular organizations but not to immigrants. Pride and a sense of belonging, marital status, and the number of children increase the likelihood of secular voluntary participation of natives but not of immigrants. These findings extend the current understanding of immigrant integration and have important implications for volunteer recruitment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.139
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.007
GPT teacher head0.259
Teacher spread0.252 · 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.

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

Citations60
Published2013
Admission routes2
Has abstractyes

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