Convergence or Polarization? 21st Century Interprovincial and Gender Based Distributional Variation in the Incomes, Ages and Education of Canadians
Bibliographic record
Abstract
Growing economic inequalities between a confederations’ constituencies can be a catalyst for the deterioration of its cohesiveness. The underlying idea is that inequalities that are more equally shared amongst a collection of subgroups, the more easily are they borne by the collection as a society. In focussing on empirical and theoretical bases for average income processes trending towards multiple or singular poles of attraction, the growth and convergence literature has long concerned itself with such issues. However, focussing on averages can be problematic since it can mask important distributional differences that can only be revealed when distributions are compared in their entirety. Here tools for examining distributional differences, exceptionalities and similarities which surmount these problems are employed in an interprovincial / gender based study of the progress of Canadian personal incomes and proxies for its latent experience and embodied human capital drivers namely age and experience. While the joint distributions of the drivers appear to be diverging, income distributions appear to be converging. However, closer inspection reveals that, when viewed separately, female and male income distributions are each diverging across the provinces, but the divergence is masked by the overall convergence of male and female distributions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| 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".