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Record W2896787808 · doi:10.1002/psp.2211

Short‐term relocation versus long‐term migration: Implications for economic growth and human capital change

2018· article· en· W2896787808 on OpenAlexaffabout
K. Bruce Newbold

Bibliographic record

VenuePopulation Space and Place · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRelocationHuman capitalTerm (time)Demographic economicsEconomic shortageHuman resourcesScheduleWorkforceGeographyEconomicsLabour economicsEconomic growth

Abstract

fetched live from OpenAlex

Abstract Driven by the growth of Canada's resource sector, interprovincial employees (IPEs), or individuals who work in one province and reside in another, have emerged as the main source for interprovincial worker mobility within Canada, with their numbers far exceeding the number of interprovincial migrants (individuals who permanently relocate from one province to another) on a yearly basis. As such, IPEs represent a significant number of workers and play an increasingly important role in the Canadian labour market, enabling individuals to respond to skill shortages and job opportunities over both the short and long term. This paper contrasts these two groups using a number of measures commonly used to characterise interprovincial migration. Results reveal that although the two groups are broadly similar, there are also subtle differences between the two groups, including differences in the age migration schedule and other sociodemographic characteristics of IPEs.

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 score0.402
Threshold uncertainty score0.799

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
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 head0.060
GPT teacher head0.348
Teacher spread0.287 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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