The customer is always right? Corporate client influence and women’s attainment in large US law firms
Bibliographic record
Abstract
Abstract Drawing on resource dependence and new institutional theories, this article examines the impact of corporate clients on the representation of women among associates and partners of large US law firms. We investigate the influence of women in corporate executive positions, women corporate chief legal officers, and clients who join in collective efforts to advocate for law firm diversity, as well as the role of power and dependence in client–firm relationships. We use longitudinal data on 665 law offices from the 2005 and 2010 editions of the NALP Directory of Legal Employers and incorporate client characteristics from the 2005 National Law Journal client list. We find that women executives among a firm’s corporate clients are linked to greater representation of women among law firm associates, but we observe no effects among partners, and scant effects of client participation in a key collective advocacy effort. Firm independence from client power is negatively linked to gender diversity among associates, while client independence from firm power is positively associated with women’s representation at both the associate and partner levels. In addition, firm independence from client power weakens the influence of female and activist corporate leaders on gender diversity in the case of associates but strengthens their influence in the case of partners.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".