O-124 Men and women at work in Canada, 1991–2016
Bibliographic record
Abstract
Introduction Women’s increased labour force participation in Canada is a well-known trend over the past 40 years, and there is a perception that the gendered division of the labour force has decreased over time. Objectives The study objective was to document the division of occupations by sex/gender in Canada and to examine the trends since 1991. The evidence is intended to inform occupational health and safety policies and procedures by including issues of sex/gender as part of the discourse on risk prevention, where warranted. Methods Data obtained from the last six Canadian Censuses of Population (1991–2016) were analyzed and descriptive statistics were used to examine the labour force composition within various resolutions of the National Occupational Classification (NOC) codes by sex/gender. GEE Poisson regression models were used to generate time- and occupation-adjusted estimates for incidence rate ratios with 95% confidence intervals for sex/gender differences in the labour force. Highly divided occupations were defined as those with 75% or more men or women. Results Of the 500 4-digit occupational categories representing 2,892 data points over the 25-year period, 58% were highly divided, with more than three-quarters of these incidences being for male-dominated occupations, with less than one-quarter being for female-dominated occupations. GEE analyses of these occupation groups within broad occupational groups suggested relative stability in the gendered nature of occupations over time, with a statistically significant reduction in the proportion of highly divided occupations only observed among occupations broadly grouped within natural and applied sciences. Conclusion The Canadian workforce remains highly sexed/gendered. While the distribution of men and women within occupations is driven by complex factors, our inquiry into these found that systems of gender essentialism, organizational hierarchies that favour men, and labour markets that are change-resistant remain challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".