The Paradox of Elite Law Schools in India — A Comparison with Canadian Legal Education
Bibliographic record
Abstract
Legal education, like law, should always be overhauled and refitted to changes in society. What is sought is a model of legal education that best meets the needs of the society, by law students and law professionals alike. In 1987, a new model of law school was established in Bengaluru, India — the National Law School of India University (NLSIU) — drawing largely upon components of the Socratic method and the case-study method that had already been implemented, tried and tested in North America. This paper is a comparison of legal education in North America, particularly in Canada, and in the National Law Universities (NLUs) in India, based on the model of NLSIU. The comparison identifies similarities and dissimilarities between legal education of two countries, India and Canada, one developed and one developing, both of which imbibed the Harvard case method at some point in time. The object of the study is to point out the paradoxes existing in legal education in general and the NLU system in India and is a preliminary study of whether Canadian law schools and NLU systems can learn lessons from each other. At one time — when law-school education was characterized by disinterested practitioners and academicians lecturing a passive group of students and evaluating them through closed-book examinations, where students needed to spend time memorizing the law instead of analyzing it — NLUs were a welcome experiment. They changed the face of legal education by encouraging discussion in class; incorporating an interdisciplinary approach, introducing research projects, compulsory internships and introducing many other innovations. With time, these innovations proved to be less effective and perhaps the time is ripe for change in legal education in India, as in the words of Roscoe Pound, “[w]e must seek principles of change no less than principles of stability.”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".