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Immigrants and homelessness—at risk in Canada's outer suburbs

2009· article· en· W2153550522 on OpenAlexaffvenueabout
Valerie Preston, Robert A. Murdie, Jane Wedlock, Sandeep Agrawal, Uzo Anucha, Silvia D’Addario, MIN J. KWAK, Jennifer Logan, Ann Marie F. Murnaghan

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsResidenceImmigrationAffordable housingMetropolitan areaRentingDemographic economicsHousing tenureRental housingCensusPublic housingHousehold incomeEconomic growthGeographySocioeconomicsBusinessPolitical scienceEconomicsSociologyPopulationDemography

Abstract

fetched live from OpenAlex

Homelessness is a risk for growing numbers of immigrants. Largely as a result of low incomes, newcomers are more likely than the Canadian‐born to spend over 50 percent of total household income on housing costs. Many newcomers suffer ‘hidden homelessness’. They do not use shelters and other services, but share accommodation, couch‐surf and rely on their social contacts for temporary and precarious housing. The adverse impact of low incomes on the housing experiences of Canadian newcomers is exacerbated in the outer suburbs of metropolitan areas where the supply of affordable housing is limited. This study explores the social backgrounds and housing experiences of immigrant households that are vulnerable to homelessness in outer suburbs through analysis of special tabulations from the 2001 census for York Region and interviews with representatives from local community organisations serving immigrant and low‐income populations. The initial findings confirm that a high proportion of newcomers in York Region are at‐risk of homelessness during the first 10 years of residence in Canada. Although renters are more vulnerable than homeowners, a substantial percentage of newcomers who are homeowners pay more than 30 percent of their total income on housing costs. The shortage of affordable rental housing in the outer suburbs exacerbates the impacts of low incomes, immigration status, household size and ethnoracial identities on immigrants' housing .

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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

Citations68
Published2009
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Geographies / Géographies canadiennesSame topicHomelessness and Social IssuesFrench-language works237,207