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Record W3023396334 · doi:10.3390/soc10020037

Welcome to Canada: Why Are Family Emergency Shelters ‘Home’ for Recent Newcomers?

2020· article· en· W3023396334 on OpenAlexfundaboutno aff
Katrina Milaney, Rosaele Tremblay, Sean Bristowe, Kaylee Ramage

Bibliographic record

VenueSocieties · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaCumming School of Medicine, University of CalgaryUniversity of Calgary
KeywordsImmigrationFocus groupService providerSociologyService (business)Political scienceCriminologyGender studiesBusiness

Abstract

fetched live from OpenAlex

Although Canada is recognized internationally as a leader in immigration policy, supports are not responsive to the traumatic experiences of many newcomers. Many mothers and children arriving in Canada are at elevated risk of homelessness. Methods: This study utilized a community-engaged design, grounded in a critical analysis of gender and immigration status. We conducted individual and group interviews with a purposive sample of 18 newcomer mothers with current or recent experiences with homelessness and with 16 service providers working in multiple sectors. Results: Three main themes emerged: gendered and racialized pathways into homelessness; system failures, and pre- and post-migration trauma. This study revealed structural barriers rooted in preoccupation with economic success that negate and exacerbate the effects of violence and homelessness. Conclusion: The impacts of structural discrimination and violence are embedded in federal policy. It is critical to posit gender and culturally appropriate alternatives that focus on system issues.

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)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0150.005
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.123
GPT teacher head0.392
Teacher spread0.269 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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