Unpacking client capture: evidence from corporate law firms
Bibliographic record
Abstract
The literature on the professions long recognized that clients can exert considerable influence over their advising professionals, with the risk that the advice they are given may not be in their best interests. In a qualitative study of 106 Canadian corporate lawyers working in large law practices we find that the traditional formulation of this problem, in terms of a client ‘capturing’ a professional, fails to address the complexities of situations in which the client is a corporation and the professional is a partner within a large professional service firm. We induce a model of client capture from our data that unpacks the concept and shows that the professional may fall under a range of influences, not all of which come from the client directly; and distinguishes between four distinct forms of capture which, in effect, trace the pathways through which the power of the client is exercised. We illustrate the nature of the resulting influences on professionals using material from the interviews, and consider the implications of our findings.
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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 | 0.033 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.021 | 0.022 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| 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".