Corporate lawyers and their clients: walking the line between law and business
Bibliographic record
Abstract
This paper examines the complex and subtle world of lawyer–client relationships. Taking corporate lawyers as our case study, we provide an examination of the strategies and tactics that lawyers use in dealing with their clients. Rather than adopting the binary distinction between professionalism and commercialism on which much past research has been based, we here take a more pragmatic approach. Informed by recent work on relational regulation by Silbey, we embrace a broader framework that incorporates lawyers' relations, organizational contexts and professional proscriptions. Based on analyses of interviews with 106 corporate lawyers working in large law firms, we demonstrate that there is a heterogeneous set of practices that characterizes corporate lawyers' relationships with clients. We observe four ideal types that run along two axes: the extent to which lawyers reference law versus experience to explain their behavior or decisions; and the extent to which lawyers frame their role in terms of individual action or as part of a collectivity. We argue that identifying ideal types allows us to open up the scope of understanding the ways in which lawyers interact with their clients.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".