Has Canada's 21st‐Century Grand Gender Convergence Stalled? Male and Female Income and Human Resource Stock Distributions Viewed Through an Equal Opportunity Lens
Bibliographic record
Abstract
Abstract The increasing similarity of male and female labor market roles in advanced economies over the past 50 years, dubbed the “Grand Gender Convergence” by Goldin (2014), appears to have stalled. Given commonality of preferences for work and human resource acquisition across the gender divide, women and men with similar human resources and efforts should have similar income distributions in a non‐discriminatory equal opportunity equilibrium. However, income convergence is a necessary but not sufficient condition for a “Grand Gender Convergence” as similarities in incomes could be achieved with differences in human resources and efforts given discriminatory rewards. In this study, using new tools for examining distributional convergence processes, the progress of Canada's 21‐st Century “Grand Gender Convergence” is examined in the context of an equal opportunity paradigm. While income convergence is almost universally apparent, human resource stock distributions appear to be diverging, with women having increasingly superior resources to men, evidence that the grand convergence is not progressing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".