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Record W3212348479 · doi:10.1111/imig.12930

Changes in selection policy and refugee welfare use in Canada

2021· article· en· W3212348479 on OpenAlexafffundabout
Lisa Kaida, Max Stick, Feng Hou

Bibliographic record

VenueInternational Migration · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsWestern UniversityMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRefugeeReceiptImmigrationVulnerability (computing)WelfarePolitical scienceDemographic economicsGovernment (linguistics)Economic growthBusinessEconomics

Abstract

fetched live from OpenAlex

Abstract With the introduction of the 2002Immigration and Refugee Protection Act(IRPA), Canada shifted its refugee selection criteria from adaptability to vulnerability. Little is known about the long‐term impact of this policy change on the economic self‐sufficiency of Government‐Assisted Refugees (GARs). Using data from the Longitudinal Immigration Database (IMDB), we compare the long‐term receipt of social assistance (SA) incomes among three GAR cohorts: those admitted pre‐IRPA (1997–2001), during the transition period (2002–2004) and post‐IRPA (2005–2009). We find GARs admitted during the transition period and post‐IRPA have higher SA rates than their pre‐IRPA counterparts three to eight years after arrival, and the gap is explained mostly by the former's lower employment rates. After year eight, however, the gap starts to decline. This suggests while post‐IRPA GARs require a more extended financial assistance, prioritising humanitarian goals in refugee admission policies does not lead to their prolonged welfare receipt.

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)0.004
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 score0.054
Threshold uncertainty score0.391

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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

Citations4
Published2021
Admission routes3
Has abstractyes

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