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Record W1532443611 · doi:10.60082/2817-5069.1222

Counting Outsiders: A Critical Exploration of Outsider Course Enrollment in Canadian Legal Education

2007· article· en· W1532443611 on OpenAlexaffvenueabout
Natasha Bakht, Kim Brooks, Gillian Calder, Jennifer Koshan, Sonia Lawrence, Carissima Mathen, Debra Parkes

Bibliographic record

VenueOsgoode Hall law journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of ManitobaUniversity of CalgaryUniversity of VictoriaDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsLegal educationDiversity (politics)Identity (music)SociologyCritical theoryPower (physics)Qualitative propertyHigher educationPolitical scienceLawPedagogy

Abstract

fetched live from OpenAlex

In response to anecdotal concerns that student enrollment in "outsider" courses, and in particular feminist courses, is on the decline in Canadian law schools, the authors explore patterns of course enrollment at seven Canadian law schools. Articulating a definition of "outsider" that describes those who are members of groups historically lacking power in society, or traditionally outside the realms of fashioning, teaching, and adjudicating the law, the authors document the results of quantitative and qualitative surveys conducted at their respective schools to argue that outsider pedagogy remains a critical component of legal education. The article situates the numerical survey results against both a critical review of the literature on outsider legal pedagogy and detailed explanations of student decision-making in elective courses drawn from student survey responses. Notwithstanding the diversity of the faculties surveyed, the authors conclude the article by highlighting some of the shared and significant findings of the research, paying attention to various identity-based, institutional, and external factors influencing critical course engagement in Canadian law schools today.

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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.426
Teacher spread0.370 · 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 designTheoretical or conceptual
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

Citations4
Published2007
Admission routes3
Has abstractyes

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