Human capital and regional convergence in Canada
Bibliographic record
Abstract
Proposes an empirical analysis of regional convergence in Canada based on the growth model of Barro et al. In an open economy with perfect capital mobility, if domestic residents cannot borrow abroad with human capital as collateral, the dynamics of human capital accumulation is the driving force of per capita income growth. Empirical results indicate that, as predicted by the theoretical model, various indicators of the stock of human capital did converge at the same speed as per capita income during the 1951‐1996 period. A substantial part of the relative growth of per capita income indicators across Canadian provinces since the early 1950s could be explained by the convergence process of human capital indicators based on the percentage of the population, both sexes and males, who have at least a university degree. The estimates of the human capital share in national income based on those indicators are in the neighbourhood of 0.5, a number consistent with other measures of the implicit income share of human capital. The convergence speed of per capita income at the regional level might have been two to three times faster, if all persons had invested in education at the same rate as the young.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".