Bibliographic record
Abstract
Abstract Save the Constitution, the United States’s most famous legal export may well be the case method. This article pieces together the story of how CC Langdell’s brainchild was brought to the rest of the common law world in treading the momentous events and geopolitics of the last century and a half, and reflects on the lessons from this global experiment for the present and future of the case method. After initially attracting little attention overseas, the rise of the case method encountered its watershed in the post-war period. From then on, the method’s further expansion branched into two narratives: one in the Western world and the other in the former British African, Asian, and Caribbean colonies. The overseas experiments with the case method yielded some durable successes and cast a light on its singular ability to adapt to widely different environments. But mostly the picture is one of mixed results, especially outside the Western world, owing to a failure to devise an educational program adapted to the varying local circumstances. As with law itself, the teaching of law is inextricably tied to local idiosyncrasies and world politics, thus making the future of the case method as difficult to predict as it would have been for Langdell and his contemporaries. Today’s different national and international contexts from that of 1945 represent an opportunity to reassess the merits of the case method in our more mature legal and educational environments, freed from colonial and neocolonial agendas.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.047 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".