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Record W2074901169 · doi:10.1080/14616718.2013.840112

Resilience and housing choices among Filipino immigrants in Toronto

2013· article· en· W2074901169 on OpenAlexaboutno aff
Ren Thomas

Bibliographic record

VenueInternational Journal of Housing Policy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
FundersAustralian Government
KeywordsImmigrationCensusMetropolitan areaPsychological resilienceRentingDemographic economicsSettlement (finance)PopulationFlexibility (engineering)Descriptive statisticsRental housingResilience (materials science)GeographyEconomic growthSociologyEconomicsPolitical scienceDemography

Abstract

fetched live from OpenAlex

In Canada, where immigration plays a major role in population growth, immigrants’ housing choices and settlement patterns have been extensively researched. Using a case study of Filipino immigrants in the Toronto Census Metropolitan Area, this paper demonstrates that choices such as affordable rental housing may contribute to flexibility and mobility in increasingly competitive labour and housing markets. The study, using descriptive statistics from Census data and interviews with Filipino immigrants, found that structural changes in immigration, housing and labour market policy over the past few decades have affected immigrants’ housing choices. These structural changes, combined with Filipinos’ resilience strategies, have resulted in housing patterns that are responsive to constantly changing household and labour market characteristics.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.341
Teacher spread0.324 · 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 designQualitative
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

Citations18
Published2013
Admission routes1
Has abstractyes

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