Shifting perspectives: the potential of rich, ill-structured problems in legal education
Bibliographic record
Abstract
Messy, real-world problems that haven’t yet been framed into a single, neat narrative contain a richness that makes them a powerful tool in legal education. This paper explores the practices of three law teachers who use ill-structured problems to help law students develop the capacity for problem setting, enabling them to structure and restructure problems from different doctrinal, conceptual and theoretical perspectives. In each course, the confrontation of legal rules or reasoning with the concrete realities of a specific situation serves as a catalyst for insight, teaching students the value of seeing and staying with complexity to gain deep understanding and potentially generate new ideas.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".