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Record W2071443924 · doi:10.1177/1746197906060713

Multiethnic neighbourhoods as sites of social capital formation

2006· article· en· W2071443924 on OpenAlexaffabout
Ranu Basu

Bibliographic record

VenueEducation Citizenship and Social Justice · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsYork University
Fundersnot available
KeywordsNeighbourhood (mathematics)Social capitalRestructuringMandateImmigrationSettlement (finance)PoliticsSociologyAsideEconomic growthSocial integrationCapital (architecture)Civil societyPolitical sciencePolitical economySocial scienceLawGeographyEconomics

Abstract

fetched live from OpenAlex

In an idealdemocratic society, publicly funded schools serve many purposes. Aside from its educational mandate, schools are places for neighbourhood integration, social capital formation and the fostering of civil society. For newly arrived immigrants, especially those with young children, schools are important sites of settlement experiences. During the past near decade, however, rapid restructuring of the public education system in Ontario has led to many changes in these ideals. Within the landscape of this wider transformation, this article critically explores how different forms of social capital are produced in schools and accessed by recent immigrants. Based on a spatial-network framework developed earlier this article examines not only how immigrants participate in the daily life of their local institutions; but whether these links are powerful enough to translate into purposeful political effects as well. The outcomes that arise from this neighbourhood-based exercise are crucial in reflecting on a larger ethical question – how do states determine who is and who is not entitled to membership in society?

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)0.001
metaresearch head score (Gemma)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0000.000
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 head0.021
GPT teacher head0.317
Teacher spread0.295 · 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

Citations8
Published2006
Admission routes2
Has abstractyes

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