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Record W1528550101 · doi:10.1111/1467-8551.12060

Digitalization and Promotion: An Empirical Study in a Large Law Firm

2014· article· en· W1528550101 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Management · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsAthabasca UniversityUniversité Laval
Fundersnot available
KeywordsBespokePromotion (chess)Context (archaeology)Work (physics)Service (business)Empirical researchMarketingLawBusinessEconomicsPublic relationsPolitical sciencePoliticsEngineering

Abstract

fetched live from OpenAlex

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 continental E urope, 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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.372
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it