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Record W2012170552 · doi:10.1080/09695958.2014.977792

Corporate lawyers and their clients: walking the line between law and business

2014· article· en· W2012170552 on OpenAlexaff
Ronit Dinovitzer, Hugh Gunz, Sally Gunz

Bibliographic record

VenueInternational Journal of the Legal Profession · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsLine (geometry)LawBusinessPolitical scienceMathematicsGeometry

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.375
Teacher spread0.324 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2014
Admission routes1
Has abstractyes

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