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Record W4396572259 · doi:10.1007/978-3-031-55680-7_11

Settlement and Rental Housing Experiences Among Recent Immigrants in the Suburbs of Vancouver: Burnaby, Richmond, and Surrey

2024· book-chapter· en· W4396572259 on OpenAlexaffabout
Carlos Teixeira, Anabel Lopez

Bibliographic record

VenueIMISCOE research series · 2024
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsRentingSettlement (finance)ImmigrationGeographyArchaeologyEngineeringBusinessCivil engineeringFinance

Abstract

fetched live from OpenAlex

Abstract The suburbanization of immigrants in Canada is a relatively recent phenomenon, and these suburban settlement experiences and residential patterns are varied and complex. This chapter explores the settlement and housing experiences of recent immigrants in Burnaby, Richmond, and Surrey – three culturally diverse and fast-growing Vancouver suburbs – as well as the interactions between suburbanization processes and the housing strategies of migrants. Securing good-quality, affordable housing is key to the successful resettlement and integration of immigrants. For this study, data were collected from questionnaire surveys administered to 137 immigrants renting in the suburbs of Vancouver. The findings revealed that transitioning from their homelands was a stressful and costly experience. Given the escalating housing costs in the rental and homeownership markets and low vacancy rates, most participants had difficulties finding housing. Participants coped by sharing housing with relatives or friends to save money or by renting a basement. They also reported financial stress, with most living in unaffordable rental housing. The low vacancy rates in Vancouver’s suburbs have created a ‘landlord’s market’ and about one-third of participants reported perceived discrimination based on income, large family size, immigrant status, and general mistrust of their cultural, religious, racial, or ethnic backgrounds.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.063
GPT teacher head0.291
Teacher spread0.228 · 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 designNot applicable
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

Citations1
Published2024
Admission routes2
Has abstractyes

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