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Record W4384929605 · doi:10.1111/cag.12869

Navigating the housing crisis: A comparison of international students and other newcomers in a mid‐sized Canadian city

2023· article· en· W4384929605 on OpenAlexafffundvenueabout
Yolande Pottie‐Sherman, Julia Christensen, Maryam Foroutan, Siyi Zhou

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsQueen's UniversityMemorial University of Newfoundland
FundersOcean Frontier Institute
KeywordsImmigrationPrejudice (legal term)Coronavirus disease 2019 (COVID-19)Political scienceSocial justiceRacismEconomic JusticeSociologyFocus groupEconomic growthCriminologyGender studiesLawEconomics

Abstract

fetched live from OpenAlex

Abstract This article investigates the housing experiences of international students in comparison to other newcomers in the mid‐sized Canadian city of St. John's, Newfoundland and Labrador, with a focus on how they navigate housing crises. Drawing on recent literature on housing justice, a quantitative survey of 188 participants, and 30 qualitative interviews, the findings reveal that international students and other newcomers are at different stages of their housing careers, have different needs and goals, and are experiencing the affordability crisis differently. Housing discrimination is a pressing concern, especially for international students who are subjected to intersectional prejudice, exploitation by landlords, and amplified challenges due to the COVID‐19 pandemic. The article argues for inclusive housing and immigration policies that acknowledge international students as part of the Canadian housing market and ensure their rights to 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 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0220.007
Scholarly communication0.0080.002
Open science0.0020.007
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.030
GPT teacher head0.258
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 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

Citations13
Published2023
Admission routes4
Has abstractyes

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