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The Legal Profession and the Market for Lawyers

2017· book· en· W2731985030 on OpenAlexaff
Albert Yoon

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLegal professionLawDebtLegal educationPerspective (graphical)Political scienceQuality (philosophy)Great recessionRecessionCommercial lawPractice of lawEmpirical legal studiesLegal researchBusinessEconomicsLabour economicsFinance

Abstract

fetched live from OpenAlex

This chapter looks at the legal profession from the perspective of law and economics. It examines the legal profession from a labour market perspective. Firstly, it looks at law schools, which serve as an initial gatekeeper for the legal profession. Within law schools, it reviews the literature on admissions, bar passage, and educational debt. Secondly, it considers the labour market for lawyers, looking at the small competitive market of judicial clerkships; the practice of law, predominantly from a large law firm perspective; and the smaller competitive markets for judgeships and legal academia. Thirdly it looks at attorney quality and performance, reviewing the literature that examines attorney quality within and across areas of law, and perceptions where disparity may be the greatest. It concludes with a discussion of the future of law in the aftermath of the 2008 global recession and recent developments in technology.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.048
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.007
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0480.005

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 head0.036
GPT teacher head0.317
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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