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Record W3114421865 · doi:10.1080/1331677x.2020.1863827

Does higher population matter for labour market? Evidence from rapid migration in Canada

2020· article· en· W3114421865 on OpenAlexaboutno aff
Siming Yu, Muhammad Safdar Sial, Malik Shahzad Shabbir, Muhammad Moiz, Peng Wan, Jacob Cherian

Bibliographic record

VenueEconomic Research-Ekonomska Istraživanja · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationDemographic economicsWageEconomicsImmigration policyPopulationLabour economicsEmpirical evidenceGeographyDemographySociology

Abstract

fetched live from OpenAlex

Canada has been a host country to migrants for decades through its attractive immigration policy. To enrich the literature, this article analyses the impact of immigration on the Canadian labour market at the regional level. For this purpose, 10 provinces of Canada have been selected for this study with the data spanning over 12 years from 2006 to 2017. Through the empirical analysis, the article finds there is a significant negative impact of immigration on the native employment level. Whereas the opposite results are found on the national level and the impact on the income of native workers is found to be negative and significant. The employed natives are also found to be migrating to other states at a higher rate in regions where immigration is higher. These results show that natives employees in the labour market tend to migrate and immigration hence offsetting the wage effects on the regional level.

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)0.006
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 score0.016
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.068
GPT teacher head0.348
Teacher spread0.280 · 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

Citations30
Published2020
Admission routes1
Has abstractyes

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