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Record W2986068837 · doi:10.1111/imig.12657

Feeling Attached and Feeling Accepted: Implications for Political Inclusion among Visible Minority Immigrants in Canada

2019· article· en· W2986068837 on OpenAlexafffundabout
Antoine Bilodeau, Stephen White, Luc Turgeon, Ailsa Henderson

Bibliographic record

VenueInternational Migration · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of OttawaCarleton UniversityConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFeelingInclusion (mineral)PoliticsImmigrationSocial psychologyValue (mathematics)PsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Immigrants’ sense of belonging to their host communities is viewed as a core condition for their successful inclusion, but there is no consensus on which attributes of belonging are most relevant to understanding inclusion, nor is there agreement on how the sense of belonging ought to be measured empirically. This study examines how two related but independent dimensions of belonging help to better understand the political inclusion of visible minority immigrants in Canada. More specifically, it examines the role of feeling attached (immigrants’ feeling toward the host community) and the role of feeling accepted (immigrants’ sense of how their host communities feel about them). We assess the relationship between attachment, acceptance and political inclusion for both first‐ and second‐generation visible minority Canadians; the results suggest there is analytical value in utilising separate measures of attachment and acceptance: political inclusion is more likely when both are stronger.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0110.005
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.316
Teacher spread0.300 · 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 designQualitative
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

Citations16
Published2019
Admission routes3
Has abstractyes

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