Changing Patterns of Gender Representation in Canada's Technology Sector and the Care Economy: Two Differing Tales
Bibliographic record
Abstract
Gender segregation is a persistent form of labour market inequality, though patterns differ across time and economic sectors. Focusing on the care economy and the technology sector, we examine longitudinal trends in gender distributions for educational credentials and occupational participation. This sector-specific analysis reveals two polarized patterns of gender segregation. In market-based care activities, labour force gender imbalance is intensifying even in the face of labour shortages. Fewer men are found in most care and communal fields of study and occupations. In the technology sector, and despite concerted efforts to improve gender balance, little change has occurred in the share of women in computing, engineering, and physics. This lack of gender change in key subfields of the technology sector is, however, often obscured by women's increasing prominence in the biological and life sciences. While there has been a historic erosion of gender segregation in Canadian schooling and the labour force, the current extent of segregation remains high, and its erosion has not only stalled in the technology sector but also in the care sector, where gender imbalance is seriously worsening. In both sectors, gender-responsive recruitment is essential, but recruitment must be nuanced and targeted to specific fields of study and occupations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".