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Record W2039043721 · doi:10.1353/dem.2000.0003

The spatial separation of the poor in Canadian cities

2000· article· en· W2039043721 on OpenAlexaffabout
Eric Fong, Kumiko Shibuya

Bibliographic record

VenueDemography · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeparation (statistics)CensusGeographyIndex of dissimilarityEthnic groupRedevelopmentPopulationSocioeconomicsDemographyEconomic growthSociologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

We used the 1991 Canadian census to examine the extent of spatial separation of the poor in Canadian cities. Although there were no extensive areas of blight, decay, or housing abandonment, we found high spatial separation of poor visible minorities in the selected cities. The index of dissimilarity indicates high segregation of poor blacks and moderate separation of poor Asians from the nonpoor population. We tested the effects of three major structural factors--racial and ethnic segregation, income segregation, and urban redevelopment--and found that racial and ethnic residential patterns are related strongly to the spatial separation of poor persons. The relationship between income segregation and spatial separation of the poor is not significant, however. We also found that the relationship between urban redevelopment and spatial separation of the poor pertains only to blacks. These findings suggest that blacks are vulnerable in the process of urban redevelopment.

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)0.004
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 score0.017
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0060.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0000.000
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 head0.011
GPT teacher head0.269
Teacher spread0.258 · 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

Citations70
Published2000
Admission routes2
Has abstractyes

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