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Record W3126051245 · doi:10.35188/unu-wider/2017/415-5

Settlement and labour force outcomes for Afghan immigrants and their children in Canada

2017· book· en· W3126051245 on OpenAlexaffabout
Ravi Pendakur

Bibliographic record

VenueWorking Paper Series · 2017
Typebook
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAfghanImmigrationEarningsSettlement (finance)Demographic economicsResidencePolitical scienceGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

Past research suggests that Afghan immigrants and their children face challenges in settlement, stemming from the impact of displacement, language barriers, poor health, limited education, limited knowledge of or access to services, and discrimination. Using data from Canada’s 2011 National Household Survey and 2009 Longitudinal Immigration Database, this paper adds to these findings, pointing to poor labour force outcomes for most Afghan immigrants as compared to other immigrants. Home ownership probabilities are found to be concomitantly poor. Sons of Afghan immigrants fare better than their fathers, but no better than other immigrant men. However, the daughters of Afghan immigrants fare much better both in terms of employment probabilities and earnings as compared to other immigrant women.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.696

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 studies0.0010.000
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 head0.011
GPT teacher head0.243
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2017
Admission routes2
Has abstractyes

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