Revolving Doors: Social Dimensions of Law Firm Culture and Pathways out of Firms
Bibliographic record
Abstract
Abstract A growing body of research suggests that contemporary law firms face challenges with the retention of legal talent—especially women and racialized lawyers. Yet, we know little about the conditions that prompt lawyers to leave law firms or where they go after leaving. This article builds on the scholarship of John Hagan, emphasizing the role of social capital in law firm culture, and work by Emmanuel Lazega, tracing dimensions of law firm collegiality—both with implications for lawyers’ careers within and beyond law firms. I draw on data from a twenty-seven-year longitudinal survey of Canadian lawyers. Using piecewise exponential survival models, I examine organizational, cultural, and individual factors that may encourage mobility from law firms. The study reveals a pervasive gender difference that is not explained by human capital, organizational characteristics, or individual traits. Results also demonstrate the importance of social capital and firm culture—specifically, the presence of workplace policies of flexible scheduling, lawyers’ sense of a good match with their firm, their satisfaction with status rewards, and finally, the role of mentors—in shaping the flow of legal talent from law firms to various job destinations.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".