Undermining Gender Equality: Female Attrition from Private Law Practice
Bibliographic record
Abstract
The number of women in the legal profession has grown tremendously over the last 40 years, with women now representing about half of all law school graduates. Despite the decades-long pipeline of women into the profession, women's representation among law firm partnerships remains dismally low. One key reason identified for women's minority presence among law firm partners is the high level of attrition of women associates from law firms. This high rate of female attrition undermines efforts to achieve gender equality in the legal profession. Using a survey of 1,270 law graduates, we employ piecewise constant exponential hazard regression models to explore gendered career paths from private law practice. Our analysis reveals that, for both men and women, the time leading up to partnership decisions sees many lawyers exit private practice, but women continue to leave private practice long after partnership decisions are made. Gender differences in leaving private practice also surface with reference to cohorts, areas of law, billable hours, firm sizes, and career gaps. Notably, working in criminal law augmented women's risk of leaving private practice, but not for men, while taking time away from practice for reasons other than parental leaves, hastens both men's and women's exits from private practice.
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 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".