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Record W2945436313 · doi:10.3389/fsoc.2019.00032

Immigration, Discrimination, and Trust: A Simply Complex Relationship

2019· article· en· W2945436313 on OpenAlexaffabout
Rima Wilkes, Cary Wu

Bibliographic record

VenueFrontiers in Sociology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImmigrationRace (biology)IndigenousPoliticsMediationForeign bornNative-BornSocial psychologySociologyEuropean Social SurveyPsychologyPolitical scienceGender studiesSocial scienceLaw

Abstract

fetched live from OpenAlex

Trust is integral to the process of immigrant integration. While previous research has considered whether discrimination has an effect on trust, no study has considered the specific extent to which immigrants experience more discrimination than the native-born and how this might matter for immigrant-native gaps in trust. To address this issue we provide the first study to use a formal mediation approach to studying the immigration, discrimination, and trust relationship. Drawing on the 2013 Canadian General Social Survey data (N=27,695) we analyze differences in three kinds of trust (generalized, specific others and political), and the role of discrimination, between Canadian-born whites, Canadian-born people of colour, foreign-born whites, foreign-born people of colour, and Indigenous people. We find that discrimination has a greater impact on social rather than political relationships. Immigrants have lower social trust in general and in others because of race-based discrimination rather than because they are immigrants per se.

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)0.011
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.025
GPT teacher head0.298
Teacher spread0.273 · 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

Citations62
Published2019
Admission routes2
Has abstractyes

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