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Record W3093866784 · doi:10.1080/09695958.2020.1830098

From partners to team leaders: tracking changes in the Canadian legal profession

2020· article· en· W3093866784 on OpenAlexafffundabout
Julie Paquin

Bibliographic record

VenueInternational Journal of the Legal Profession · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVariety (cybernetics)Legal professionField (mathematics)Work (physics)PerceptionPublic relationsTracking (education)SociologyPolitical scienceLawEpistemologyEngineeringComputer sciencePedagogy

Abstract

fetched live from OpenAlex

This article presents an effort to transcend the law vs business dichotomy that usually tends to prevail in discussions on the future of the legal profession, by identifying the various logics to which lawyers are exposed. It uses a computer-assisted analysis of trade magazines from 1985 to 2015 to document the changes that have taken place in lawyers’ perceptions of their work and their role in society over the last thirty years. The results show that, although a professional logic can be found in Canadian lawyers’ discourse, Canadian lawyers are exposed to a variety of logics that provide them with new vocabularies and frames of reference. The decreasing importance of the “professional” discourse suggests that significant changes may be about to take place in the field, as actors develop new strategies to legitimize alternative practices and work configurations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.937
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0140.012
Science and technology studies0.0110.003
Scholarly communication0.0070.003
Open science0.0030.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.134
GPT teacher head0.464
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2020
Admission routes3
Has abstractyes

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