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Record W4319799438 · doi:10.1080/1369183x.2023.2171974

Multigenerational living and children’s risk of living in unaffordable housing: differences by ethnicity and parents’ marital status

2023· article· en· W4319799438 on OpenAlexafffundabout
Kate H. Choi, Sagi Ramaj

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of TorontoWestern University
FundersSocial Sciences and Humanities Research Council of CanadaWestern University
KeywordsEthnic groupMarital statusPsychologyGerontologyDemographyMedicineSociology

Abstract

fetched live from OpenAlex

A growing share of Canadian households are living in unaffordable housing (i.e. spending 30% or more of their pre-tax income on housing costs). During this time, the prevalence of multigenerational living has also increased. Ethnic minority families are more likely than White families to live in multigenerational households. These trends raise the questions: (a) is multigenerational living a strategy for families to navigate the housing affordability crisis? (b) do ethnic minority children benefit more from multigenerational living than their White peers? Using confidential data from the 2016 Canadian Census, we examine how multigenerational living shapes the housing experiences of children under the age of 16. Multigenerational living is associated with consistent reductions in children’s odds of living in unaffordable housing. Yet, for those in single-parent families, this protective association is largest among White children. For those in dual-parent families, this protective association is largest among Black children. Socioeconomic disadvantage and a greater propensity for three-generation families to reside in metropolitan areas with expensive housing appear to suppress the benefits emerging from multigenerational living.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.038
GPT teacher head0.328
Teacher spread0.290 · 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

Citations9
Published2023
Admission routes3
Has abstractyes

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