Work Hard, Play Hard?: A Comparison of Male and Female Lawyers' Time in Paid and Unpaid Work and Participation in Leisure Activities
Bibliographic record
Abstract
Les auteures tentent de déterminer le temps que les professionnels, hommes et femmes, passent à effectuer du travail rémunéré ou non, et la façon dont cela influe sur leur participation à différentes activités de loisirs. Elles se fondent sur des données provenant d'avocats professant dans différents milieux juridiques. Elles constatent que les hommes rapportent consacrer plus de temps au travail rémunéré et aux loisirs, alors que les femmes accordent plus de temps aux travaux ménagers ainsi qu'aux soins des enfants. Les résultats semblent démontrer que les occasions dans l'ensemble plus importantes de loisirs chez les hommes comparées à celles des femmes seraient attribuables à des relations inattendues entre la participation des hommes aux travaux domestiques et aux soins des enfants, et leurs activités de loisirs. Les auteures présentent différentes explications à ces résultats. There has been a considerable amount of research that documents how women and men spend their time in different work and home tasks. We examine how much time professional women and men spend in paid and unpaid work and how this relates to their participation in different leisure activities. We also explore whether time in paid and unpaid work has gender‐specific effects on leisure participation. In examining these issues, we rely on data from lawyers working in different legal settings. Our results show that, as hypothesized, men report more time in paid work and leisure whereas women devote more time to housework and childcare. An unexpected finding is that the time men spend in housework or childcare is either unrelated or positively related to their leisure participation. These results suggest that men's greater overall opportunities for leisure compared with women's appear to stem from the unanticipated relationships between men's involvement in housework and childcare and their leisure activities. We raise several possible explanations for these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".