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Record W2280245510 · doi:10.1177/0002764216632833

Participation in Voluntary Associations and Social Contact of Immigrants in Canada

2016· article· en· W2280245510 on OpenAlexaffabout
Eric Fong, Jing Shen

Bibliographic record

VenueAmerican Behavioral Scientist · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocioeconomic statusHomophilyImmigrationVoluntary associationTurnoverPerspective (graphical)General Social SurveySocial contactAssociation (psychology)Social integrationDemographic economicsPsychologySocial psychologyDemographySociologyGeographyPolitical sciencePopulation

Abstract

fetched live from OpenAlex

We explore how participation by immigrants in voluntary groups is related to their social contact patterns. Our discussion is guided by the structural integration and the homophily perspectives. Drawing from the 2008 Canadian General Social Survey, the findings in general support the structural integration perspective. The findings also show that frequency of participation in voluntary groups and number of voluntary associations participated have independent and significant positive relationships with socioeconomic range of contact and number of high-status contacts, except that the number of voluntary associations involved does not relate to the number of high-status contacts among immigrants. In addition, the findings show that receiving education overseas does not relate to the range of contact and high-status contact. However, visible minority immigrants significantly has lower socioeconomic range of contact than other immigrants even controlling for voluntary association participation.

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)0.000
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 score0.062
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.331
Teacher spread0.308 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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