Is there a Grand Gender Convergence in Canada? – The Jury is Still Out
Bibliographic record
Abstract
increasing similarity of male and female roles in the labour market over the last 50 years has been dubbed The Gender Convergence, though there is concern that the process has stalled. In the absence of gender discrimination and assuming similar preferences for work and human resource acquisition across the gender divide, females and males with similar human resource characteristics should have similar income distributions in equilibrium, in effect there would be equality of opportunity across the gender divide. If that equilibrium is stable, convergence to the equilibrium state should see increasingly similar gender based income distributions accompanied by increasingly similar gender based human resource distributions. Viewed through the lens of an equal opportunity imperative, income convergence is a necessary, but not sufficient condition for a Grand Gender Convergence since similarities in income distributions could be achieved with gender based differences in human resources and efforts given a discriminatory rewards structure. Here, using new tools for empirically examining distributional convergence processes, the existence of a Grand Gender Convergence in 21st century Canada is examined in the context of such an Equal Opportunity paradigm. While income convergence is almost universally apparent, the same is not true for human resource stocks which appear to be diverging, raising questions about the existence of a Canadian Gender convergence.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".