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Record W3124626183

An Analysis of the Ins and Outs of Migration within Canada

2019· article· en· W3124626183 on OpenAlexaboutno aff
Gazi Mohammad Jamil, Damba Lkhagvasuren, Paul Gomme

Bibliographic record

VenueCIRANO Project Reports · 2019
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWageDemographic economicsRevenueAffect (linguistics)Labour economicsBusinessEconomicsPolitical scienceSociologyFinance
DOInot available

Abstract

fetched live from OpenAlex

Most studies of Canadian inter-provincial labor flows have asked how regional wage differences affect workers mobility. Relatively little is know about the characteristics of these movers. We fill in these facts. Inter-provincial migrants tend to be both younger and better educated than their peers. We document that those moving out of a province tend to earn more than otherwise similar workers who stay. Similarly, those moving into a province typically earn more than similar workers already in that province. For Quebec, we find that those moving in earn 19.1% more than otherwise identical workers in Quebec while those moving out earned 16.6% more. Such worker flows have clear implications not only for provincial tax revenues, but also for public spending in areas like education and medical and social services. Knowing the characteristics of inter-provincial migrants may aid in the design of policies to attract and retain high income 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.

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)0.000
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 score0.033
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.008
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.027
GPT teacher head0.362
Teacher spread0.336 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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