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Record W7099209052

The Impact of Interprovincial Migration on Aggregate Output and

2007· article· en· W7099209052 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityNet migration rateReal gross domestic productProduction (economics)Real wages
DOInot available

Abstract

fetched live from OpenAlex

Interprovincial migration has played an increasing role in Canada’s economy over the last three years. This report attempts to estimate the number of workers moving in and out of each province, and to estimate the total output gains due to interprovincial migration. It breaks output gains into two sources: gains due to increased employment, and gains due to re-allocation of workers between provinces with lower productivity to provinces with higher productivity. A record number of people, 370,791, equivalent to 1.14 per cent of all Canadians, migrated between provinces in 2006. This is 50 per cent more than in 2003. In 2006, the net output gains arising from interprovincial migration are estimated to be $883.1 million (1997 constant prices), or 0.074 per cent of GDP. Higher employment rates in provinces experiencing a net positive balance of interprovincial migrants were responsible for $398.0 million of the gains and higher output per worker in these provinces was responsible for $485.0 million. Interprovincial migration was also responsible for 2.82 per cent of real GDP growth over in 2006 and 1.27 per cent of real GDP growth from

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)0.003
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 score0.314
Threshold uncertainty score0.631

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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
Published2007
Admission routes1
Has abstractyes

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