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Record W2981267825 · doi:10.1111/imig.12650

Ethnic and National Sense of Belonging in Canadian Society

2019· article· en· W2981267825 on OpenAlexafffundabout
Zheng Wu, Vivien W. Y. So

Bibliographic record

VenueInternational Migration · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of VictoriaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMulticulturalismAcculturationEthnic groupImmigrationLoyaltyCultural assimilationSociologyGender studiesPolitical scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

Abstract There has been long‐standing debate among Western nations regarding the best approaches for the integration of immigrants into host societies. The core of this debate is between the proponents of assimilation and multiculturalism. Using a large sample of Canadians, we investigated the link between their sense of belonging to their ethno‐racial heritage (ethnic belonging) and to Canada (national belonging) in order to seek answers to the question of whether multiculturalism policies work to strengthen or weaken residents’ loyalty to the nation. Our analyses showed that increases in ethnic belonging significantly predicted increases in national belonging, both for ethno‐racial minorities and Whites, after controlling for demographic variables. These findings extend our understanding of acculturation and integration, provide empirical support for multiculturalism, and suggest that active support of immigrant and non‐immigrant individuals in maintaining connections to their ethno‐racial heritage increases individuals’ loyalty to the nation.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.031
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.378
Teacher spread0.346 · 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

Citations13
Published2019
Admission routes3
Has abstractyes

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