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Record W2623283223 · doi:10.1177/0042098017708939

Co-ethnic concentration and trust in Canada’s urban neighbourhoods

2017· article· en· W2623283223 on OpenAlexaffabout
Zheng Wu, Feng Hou, Christoph M. Schimmele, Adam Burke Carmichael

Bibliographic record

VenueUrban Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEthnic groupRacial diversityDiversification (marketing strategy)Diversity (politics)SociologyPopulationDemographic economicsCultural diversitySocial psychologyDemographyPsychologyEconomicsBusinessAnthropology

Abstract

fetched live from OpenAlex

This study investigates the relationship between the density of people’s ethno-racial in-group in their neighbourhoods (co-ethnic concentration) and trust in their neighbours. Previous studies demonstrate that ethno-racial diversity decreases trust in others, however, these studies rely on overly broad definitions of diversity and of trust, and often do not disaggregate the effects for Whites and ethno-racial minorities. Hence, this study examines the relationship between co-ethnic concentration and trust, focusing on how this relationship may change depending upon one’s ethno-racial status. Putnam’s (2007) analysis leads to a paradox in the sense that, according to the same principle that predicts declining trust amongst Whites, increasing diversity should lead to greater levels of trust for ethno-racial minorities whose share of the population increases with diversification. The findings demonstrate that there is a positive relationship between co-ethnic concentration and trust in neighbours and that this relationship holds for Whites as well as ethno-racial minorities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.077
GPT teacher head0.358
Teacher spread0.281 · 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.

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
Published2017
Admission routes2
Has abstractyes

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