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Record W3123379227 · doi:10.20381/ruor-25519

Human Capital, Urbanization, and Canadian Provincial Growth

2001· preprint· en· W3123379227 on OpenAlexaffabout
Serge Coulombe

Bibliographic record

VenueuO Research (University of Ottawa) · 2001
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHuman capitalEconomicsConditional convergenceUrbanizationPer capitaShock (circulatory)Per capita incomeDemographic economicsConvergence (economics)Capital (architecture)GeographyEconomic growthPopulationDemography

Abstract

fetched live from OpenAlex

This paper investigates the conditional convergence of both human capital indicators and nominal per capita income across Canadian provinces in a panel-data empirical framework. Long-run relative provincial steady states are determined by relative rates of urbanization, one-time shocks to Quebec’s and Alberta’s relative steady states, and a Nova Scotia fixed effect. Indicators of relative human capital ratios appear to have converged following a pattern that is common and similar to per capita income but with two notable exceptions. First, in Alberta, the 1973 oil shock contributed to the rise in per capita income but its effect on human capital is significant only for females. Second, human capital appears to remain concentrated in the relatively poor province of Nova Scotia. Two notable findings come out of the analysis. First, nominal income disparities at the provincial level appear to be real, not just nominal. Second, the analysis suggests that at the regional level, human capital is a necessary but not sufficient condition for being wealthier in the long run.

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)0.005
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 score0.972
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.239
Teacher spread0.184 · 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
Published2001
Admission routes2
Has abstractyes

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