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Record W2134614409 · doi:10.1002/psp.1991

Neighbours Helping Neighbours in Multi‐ethnic Context

2015· article· en· W2134614409 on OpenAlexaffabout
Eric Fong, Feng Hou

Bibliographic record

VenuePopulation Space and Place · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsStatistics CanadaUniversity of Toronto
Fundersnot available
KeywordsNeighbourhood (mathematics)Ethnic groupFriendshipSocial capitalImmigrationCensusSociologyPopulationInterpersonal tiesSocial relationDemographic economicsSocial psychologyGeographyPsychologyDemographySocial scienceEconomics

Abstract

fetched live from OpenAlex

Abstract A key topic in population and urban studies is neighbourhood social relations. The topic has significant implications for the larger debate about friendship patterns in contemporary North American society. Ties among neighbours provide social support, foster social relations, and facilitate social capital. Our study explores how the exchange of favours among neighbours, a key component in developing and maintaining social relations among neighbours, is related to co‐ethnic proportion, length of time in the neighbourhood, and family life cycle. Our discussion differentiates between relations within groups and overall relations in the neighbourhood. We merged the 2008 Canadian General Social Survey with 2006 Canadian census tract data to explore these issues. The findings present an optimistic view of a diversified society. Most members of the groups included in the analysis experience favour exchange with neighbours. Although co‐ethnic proportion and duration in the neighbourhood do not relate to favour exchange in neighbourhoods for minorities and immigrants, these factors are significant for local‐born population. In addition, as minorities and immigrants marry, their family needs may encourage them to develop social relations with neighbours. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.127
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.140
GPT teacher head0.366
Teacher spread0.226 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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