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Record W3108267369 · doi:10.1017/s0142716420000478

In search of a “home”: Comparing the housing challenges experienced by recently arrived Yazidi and Syrian refugees in Canada

2020· article· en· W3108267369 on OpenAlexaffabout
Pallabi Bhattacharyya, Sally Ogoe, Annette Riziki, Lori Wilkinson

Bibliographic record

VenueApplied Psycholinguistics · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRefugeeAgency (philosophy)Settlement (finance)Syrian refugeesPsychologyFace (sociological concept)Economic growthPolitical scienceSociologyBusinessSocial science

Abstract

fetched live from OpenAlex

Abstract Housing that is affordable and appropriate is a necessity for successful integration for all newcomers. It is not uncommon for newcomers to Canada to report difficulties finding suitable, safe, and affordable housing for their families. For refugees, however, the challenges are sometimes greater. Settlement organizations and refugee sponsors experience various challenges in accommodating families with large numbers of children, but as our research shows, refugee groups have differing needs based on their culture, family composition, and experience of trauma. Using data collected from two recent studies, we identify and compare the housing needs of two newly arrived groups of refugees to Canada: Syrians and Yazidis from northern Iraq. All participants in our study have lived in Canada for 2 years or less and currently live in Alberta, Saskatchewan, Manitoba, or Ontario. Data was collected either by face-to-face surveys (with Syrian participants) or unstructured interviews (with Yazidi women) conducted in Arabic, Kurmanji, or English. We discuss their experiences of living in resettlement centers and their transition to independent housing. In addition, we discuss how family composition and previous trauma influence their housing experiences with special attention to how increasing agency increases satisfaction with 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.496
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.090
GPT teacher head0.399
Teacher spread0.309 · 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 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

Citations12
Published2020
Admission routes2
Has abstractyes

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