Career stage dependent effects of law firm governance: A multilevel study of professional-client misconduct
Bibliographic record
Abstract
Are governance practices employed by professional service firms equally effective in preventing professional-client misconduct for professionals at different stages of their career? Drawing upon professional-agency theory and the literature documenting professional career patterns, we develop a multilevel theoretical model to answer this question. We test our model in the empirical context of the Dutch legal profession, using firm-level survey data on 142 law firms and individual-level archival data from the 2994 lawyers working for these firms to explain 97 formally adjudicated complaints of professional-client misconduct committed by individual lawyers registered with the Amsterdam Bar Association. We find that the ‘orthodox’ distinction between informal behavioral and formal outcome-based governance practices is too course-grained to receive empirical support, and that firm-level governance practices only reduce professional-client misconduct when they are specifically targeted at the career stage of the lawyers employed. Our findings not only allow us to develop a finer-grained version of Sharma’s professional-agency model, but may also be practically useful in developing firm-level governance practices targeted at different strata of professionals.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".