2002) “Occupational Gender Segregation and Women's Wages in Canada: An Historical Perspective” Cirano Working Paper
Bibliographic record
Abstract
Nous traçons un portrait de l’évolution de la ségrégation professionnelle selon le sexe au 20ième siècle, et de ses conséquences sur la condition féminine dans le marché du travail. Dans la première partie du 20ième siècle, la ségrégation professionnelle hiérarchique ou verticale a considérablement décliné alors que les travailleuses quittaient les emplois de domestique et du secteur manufacturier en faveur des emplois de bureau. Ceci créa néanmoins une importante ségrégation professionnelle horizontale qui persiste jusqu’à aujourd’hui. Pour étudier les effets de la ségrégation professionnelle sur l’écart salarial selon le sexe, nous présentons une technique de décomposition qui divise l’écart salarial en deux composantes: l’une due aux différences intra-occupations et l’autre due aux différences inter-occupations. Depuis le début des années 90, la composante intra-occupation est prédominante. We document the evolution of occupational gender segregation and its implications for women’s labour market outcomes over the twentieth century. The first half of the century saw a considerable decline in vertical segregation as women moved out of domestic and manufacturing work into clerical work. This created a substantial amount of horizontal segregation that persists to this day. To study the effects of occupational segregation on the gender gap, we introduce a decomposition technique that divides the gap into between-occupation and within-occupation components. Since the 1990s the component attributable to within-occupation wage differentials has become predominant.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".