Digitalization and Promotion: An Empirical Study in a Large Law Firm
Bibliographic record
Abstract
In law firms, the number of hours that associates work reportedly plays a preponderant role in promotion decisions. We build on previous research in this area by distinguishing the effect of ‘development hours’ from ‘billable hours’ on promotions and by assessing the extent to which billable hours are still important criteria today, in digitalized environments where efficiency is, presumably, likely to matter more than working long hours. We also examine whether certain types of behaviours, like associates' interactions with technology, may be associated directly or indirectly with a higher likelihood of promotion. We studied these questions in the context of a large corporate law firm in continentalEurope, focusing on the promotion of 93 lawyers between 2005 and 2010. We found that both billable and development hours are still significant positive predictors of promotions and that associates' ability to use the case firm's computer‐mediated knowledge management system productively is indirectly rewarded by promotion. This research reasserts the fundamental role of billable hours as one of the primary means for evaluating lawyers' work and suggests that using knowledge management systems gives associates an edge in the race for promotion, particularly in law firms moving along the ‘evolutionary path’ of legal service, from bespoke to commoditized work (Susskind, R. (2010).The End of Lawyers? Rethinking the Nature of Legal Services. Oxford: Oxford University Press).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".