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Record W2057144981 · doi:10.1155/2011/171927

Would an Increase in High‐Skilled Immigration in Canada Benefit Workers?

2011· article· en· W2057144981 on OpenAlex
Maxime Fougère, Simon Harvey, Bruno Rainville

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEconomics Research International · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsEmployment and Social Development Canada
FundersHuman Resources and Skills Development Canada
KeywordsImmigrationHuman capitalWelfareLabour economicsIncentiveEconomicsPer capitaProductivityRaising (metalworking)General equilibrium theoryWork (physics)Demographic economicsEconomic growthPopulationMarket economy

Abstract

fetched live from OpenAlex

This study examines the economic and welfare effects of raising the number of high‐skilled immigrants in Canada. It uses a life‐cycle applied general equilibrium model with endogenous time allocation decisions between work, education, and leisure. According to the simulation results, raising the number of high‐skilled immigrants would boost productive capacity and labour productivity but could lower real GDP per capita. In addition, by raising the supply of high‐skilled workers, more high‐skilled immigrants would reduce the skill premium and the return to human capital. This in turn would lower incentives for young adults to invest in human capital and have a dampening effect on the domestic supply of skilled workers. Finally, it is found that more high‐skilled immigrants would be welfare enhancing for medium‐ and low‐skilled workers but welfare decreasing for high‐skilled workers.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.343
Teacher spread0.284 · 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