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Record W1977352464 · doi:10.1080/0042098002285

Relations between Deprivation and Immigrant Groups in Large Canadian Cities

2000· article· en· W1977352464 on OpenAlexaffabout
David Ley, Heather A. Smith

Bibliographic record

VenueUrban Studies · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImmigrationCensusGeographyDemographic economicsPopulationEconomic geographyDemographySocial deprivationSociologyEconomic growthEconomics

Abstract

fetched live from OpenAlex

With the co-existence of social polarisation and unprecedented immigration during recent years in major Canadian cities, this paper examines relationships between urban deprivation and the immigrant population in 1991, compared with 1971, the end of the era of the 'old' migration. Census tracts in Toronto, Montreal and Vancouver that experienced multiple deprivation are identified. Only two tracts in all three cities displayed the full set of indicators in 1991, and none in 1971. Indicators neither overlap, nor are as spatially contained, nor are as stable over time as has been true for cities in the US. Like northern Europe, there is evidence of a suburbanisation of deprivation, linked in particular to the diffusion of state-subsidised housing, especially in Toronto. In addition, and also like Europe, there are positive relationships with immigrant populations. But these relations are modest, and affect primarily recent arrivals and non-English-speaking groups. The implications of immigration are complex, because immigrants themselves are highly heterogeneous. Moreover, a longitudinal model of socio-spatial mobility rather than socio-spatial entrapment remained the dominant immigrant experience in Canadian cities.

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)0.003
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 score0.025
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.289
Teacher spread0.252 · 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

Citations117
Published2000
Admission routes2
Has abstractyes

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