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<scp>L</scp>iving on the “edge of the suburbs” of Vancouver: A case study of the housing experiences and coping strategies of recent immigrants in Surrey and Richmond

2013· article· en· W1881939043 on OpenAlexafffundvenueabout
Carlos Teixeira

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersFederation of Canadian Municipalities
KeywordsOvercrowdingRental housingAffordable housingImmigrationEconomic rentPublic housingGentrificationRentingDemographic economicsPopulationEconomic growthBusinessLabour economicsGeographyPolitical scienceSociologyEconomicsDemographyMarket economy

Abstract

fetched live from OpenAlex

Abstract This study examines the housing experiences and coping strategies of low‐income recent immigrants in Richmond and Surrey, two fast‐growing outer suburbs of Vancouver where the immigrant population has increased rapidly in the last two decades and where there is a limited supply of affordable rental housing, including public and social housing. The study draws on data from seven focus groups with 88 recent immigrant renters and interviews with 15 key stakeholders, conducted in Vancouver, Richmond, and Surrey in 2010. The evidence indicates that these newly arrived immigrants face numerous difficulties in the rental housing market, such as high rents, overcrowding, and poor‐quality housing. Most of these immigrants were spending more than half of their monthly household income on housing, putting them at risk of homelessness. The study's findings suggest that the housing crisis affecting Surrey and Richmond—due to a limited supply of affordable rental housing and high housing costs—makes these two cities challenging regions of Vancouver for newcomers to settle in.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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

Citations47
Published2013
Admission routes4
Has abstractyes

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