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Record W2896840138 · doi:10.1111/imig.12517

Integration and Retention of Refugees in Smaller Communities

2018· article· en· W2896840138 on OpenAlexafffundabout
Tony Fang, Halina Sapeha, Kerri Claire Neil

Bibliographic record

VenueInternational Migration · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of TorontoMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsRefugeeMarket integrationImmigrationSocial capitalDestinationsPerceptionPolitical scienceCapital (architecture)Demographic economicsEconomic growthDevelopment economicsEconomicsGeographyLawPsychologyTourism

Abstract

fetched live from OpenAlex

Abstract While advanced economies attempt to pursue a regionalized immigration policy, which aims at shifting migration flows away from the most popular urban centre destinations to smaller communities, the experiences of immigrants settling in such locations remains underexplored. This research provides timely knowledge of refugee labour market integration in smaller communities, using Newfoundland and Labrador's provincial capital, St. John's, as an example of such communities. The article examines the resettlement and labour market integration of refugees in a medium‐sized city with particular attention to factors that enhance refugee labour market integration and factors that negatively impact refugee integration and their retention in the receiving community. The study finds that the negative perception of employment opportunities is a significant factor in refugee's decision to move. Securing employment of refugees is facilitated by strong English language skills, social connections and is hampered by discrimination in the labour market.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.029
GPT teacher head0.326
Teacher spread0.298 · 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

Citations50
Published2018
Admission routes3
Has abstractyes

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