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Record W3213726003 · doi:10.32920/ryerson.14656455.v1

Immigrants, Refugees and the Risk of Homelessness: Analyzing the Barriers to Adequate and Affordable Housing

2021· preprint· en· W3213726003 on OpenAlexaffabout
Ana Raicevic

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsAffordable housingImmigrationRefugeeOppressionSettlement (finance)RentingGovernment (linguistics)Economic growthRental housingPolitical scienceSocial exclusionQualitative researchPublic housingOrder (exchange)BusinessSociologyEconomicsPoliticsLawFinance

Abstract

fetched live from OpenAlex

This qualitative study examines the risk of homelessness amongst recent immigrant and refugee populations in the Greater Toronto Area by analyzing the various barriers which hinder newcomer access to adequate and affordable housing. This study incorporates the framework of Anti-Oppressive Practice (AOP) to understand the oppression, marginalization, and exclusion that many recent immigrants and refugee claimants experience within Toronto’s housing and rental markets and subsequently, how this initiates their cycle of homelessness. The findings of this study are informed by two semi-structured, informal interviews with housing and settlement workers in order to provide a working insight onto the issues that are affecting their newcomer clients on a daily basis. This study identifies challenges within Toronto’s housing market and highlights solutions put forth by housing and settlement workers. Similarly, this study examines initiatives put forth by the municipal government to address the barriers to accessing adequate and affordable 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.353
Teacher spread0.331 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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