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Record W2345145232 · doi:10.29173/alr459

The Dean Who Went to Law School: Crossing Borders and Searching for Purpose in North American Legal Education, 1930–1950

2016· article· en· W2345145232 on OpenAlexafffundvenueabout
Eric M. Adams

Bibliographic record

VenueAlberta Law Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsLegal educationBorder crossingLawPolitical scienceLegal historySociologyImmigration

Abstract

fetched live from OpenAlex

This article is about the making of modern legal education in North America. It is a case study of the lives of two law schools, the University of Alberta, Faculty of Law and the University of Minnesota Law School, and their respective deans, Wilbur Bowker and Everett Fraser, in the decades surrounding the Second World War. The article follows Bowker’s unorthodox route to Alberta’s deanship via his graduate training under the experimental “Minnesota Plan” — Fraser’s long-forgotten effort to place public service at the centre of American legal education. In detailing an overlooked moment of transition and soulsearching in North American legal education, this article underlines the personalities, ideologies, circumstances, and practices that combined to forge the still dominant model of university-based legal education across the continent. Highlighting the movement of people and ideas, this study corrects a tendency to understand the history of law schools as the story of single institutions and isolated visionaries. It also reveals the dynamic ways in which law schools absorbed and refracted the period’s ideological and political concerns into teaching practices and institutional arrangements. In bold experiment and innate conservatism, personal ambition and institutional constraints, and, above all else, faith in the power of law and lawyers, the postwar law school was born.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.027
GPT teacher head0.411
Teacher spread0.384 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2016
Admission routes4
Has abstractyes

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