New Evidence of Convergence Across Canadian Provinces: The Role of Urbanization
Bibliographic record
Abstract
This paper uses the conditional convergence model for explaining the relative evolution of per capita income across the ten provinces of the Canadian federation between 1950 and 1996. Provincial relative per-capita income steady states are determined by the relative rates of urbanization. Empirical results indicate that the provinces have converged at a speed of about 5 percent per year and, since the mid-1980s, most provinces appeared to be in the neighborhood of their respective steady state. The analysis also indicates that for the provinces of Alberta and Québec, the convergence process was disturbed in the 1970s by different permanent level shocks to their long-run steady states. / Nouveaux résultats portant sur la convergence des provinces canadiennes : le rôle de l’urbanisation - Dans la présente étude, nous tentons d’expliquer l’évolution relative du revenu par habitant entre les dix provinces canadiennes de 1950 à 1996 en utilisant le modèle de convergence conditionnelle. En régime de croissance équilibrée, le revenu provincial relatif par habitant d’une province est principalement déterminé par son taux relatif d’urbanisation. Les résultats de l’analyse empirique indiquent que les économies provinciales ont convergé à un taux annuel moyen d’environ 5 % et que, depuis le milieu des années 1980, la plupart des provinces avoisinent le niveau de leur régime de croissance équilibrée. Cependant, l’analyse montre que dans le cas de l’Alberta et du Québec, le processus de convergence a été perturbé par un bris structurel dans les années 1970 qui s’est traduit par un choc une fois pour toutes sur le niveau du régime de croissance équilibrée.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".