Gendered Inequalities in Earnings: A Study of Canadian Lawyers*
Bibliographic record
Abstract
Cette étude analyse les salaires des avocats et explore si, et pourquoi, les hommes et les femmes reçoivent un traitement salarial différent. Un modèle, tiré de la théorie du human capital et de la théorie de la segmentation des occupations, est proposé. Malgré le fait que le sexe des avocats n'a pas d'effet direct sur leur salaire, les femmes sont désavantagées par rapport à plusieurs facteurs qui augmentent de façon significative les salaires de leurs collègues masculins. Plus spécifiquement, les avocates ont moins d'expérience dans la pratique du droit, travaillent des heures plus courtes, sont moins nombreuses à avoir des enfants d'âge préscolaire et ont moins d'autonomie dans leur travail que leurs homologues masculins. Les résultats demontrent aussi que les avocats et avocates ne sont pas rémunérés différemment pour leurs investissements en capital humain, mais nous suggérons que la discrimination salariale opère de façon plus subtile. Nous faisons aussi des recommandations quant aux recherches à venir. This study examines lawyers' earnings and explores if and why male and female lawyers are differentially rewarded. A model is proposed that draws from human capital theory and occupational segmentation theory. Although lawyers' sex does not have a direct impact on earnings, women were found to be disadvantaged along many of the factors that significantly increased lawyers' earnings. Specifically, women in law have less experience practising law, work shorter hours, are less likely to have preschool‐aged children, and have less job autonomy than their male counterparts. The results also show that male and female lawyers are not differentially rewarded for their human capital investments, but we suggest that pay discrimination may be operating in more subtle ways. Recommendations for future research are presented.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".