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Record W3090394908 · doi:10.1177/2322005820946698

Comparing Experiential Legal Education in Canada and India

2020· article· en· W3090394908 on OpenAlexaboutno aff
Rhea Roy Mammen

Bibliographic record

VenueAsian Journal of Legal Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsLegal educationExperiential learningContext (archaeology)Experiential educationGlobePolitical scienceLawPublic relationsPsychologyGeography

Abstract

fetched live from OpenAlex

Legal education has evolved over several centuries across the globe, and its effectiveness is a matter of significant concern not merely for legal practitioners but also for society in general. One approach that has been gaining considerable attention is the concept of experiential legal education, which is at different levels of implementation across the world. Countries such as the United States and Canada have been pioneers in implementing this form of legal education, which is also known as clinical legal education (CLE), whereas India is striving to catch up. This article attempts to inspect and compare the development and implementation of CLE in Canada and India. The findings from the comparison are then utilized to inform the way ahead for CLE in India. While pursuing this objective, the article also examines the concept of experiential education, in general, and in the context of legal education, in particular. Moreover, insights are provided regarding CLE. The status of experiential legal education in Canada is reviewed, and the author’s experience in Canada under the Shastri Research Student Fellowship (SRSF) is detailed to provide the author’s insights regarding the implementation of experiential legal education in Canada. The evolution of experiential legal education in India is also detailed, together with insights regarding the regulations of the Bar Council of India (BCI) as are relevant to CLE. Finally, the article compares the author’s opinion of the present status of CLE in Canada and India and provides recommendations to enhance the future implementation of CLE in India.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.023
GPT teacher head0.337
Teacher spread0.314 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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