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Record W57287063 · doi:10.29173/alr336

Who is Afraid of the Big Bad Social Constructionists? Or Shedding Light on the Unpardonable Whiteness of the Canadian Legal Profession

2008· article· en· W57287063 on OpenAlexaffvenueabout
Charles C. Smith

Bibliographic record

VenueAlberta Law Review · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsAlzheimer Society of CanadaThe Scarborough HospitalCanadian Centre for Policy AlternativesUniversity of Toronto
Fundersnot available
KeywordsLegal professionDiversity (politics)Statutory lawEquity (law)Political scienceLawPopulationOrder (exchange)SociologyBusiness

Abstract

fetched live from OpenAlex

This article considers the lack of racial diversity in the legal profession, which is lower than other comparable professions. The author focuses on accessibility to the legal profession and entry into the practice. Historically, access was limited, and sometimes prohibited, by discriminatory social and statutory barriers. Tuition for post-secondary education is now a central barrier that increasingly divides along racial lines, due to the nexus between class and race. Despite the attention given to the problem of lack of racial diversity in the legal field through reports, task forces, surveys, and so forth, there is still much progress needed in order to ensure that the diversity ofthe profession reflects the makeup of the country's population. The author advocates the elimination of barriers to legal education and subsequent entry into the profession through cooperative initiatives between schools, firms, legal associations, and community organizations in order to increase racial equity and diversity within Canada’s legal profession.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.348
Teacher spread0.295 · 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
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
Published2008
Admission routes3
Has abstractyes

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