Crossing Borders in the Classroom: A Comparative Law Experiment in Family Law
Bibliographic record
Abstract
What if law students in Mexico could talk to law students in Canada about same-sex marriage? What if law students in Texas could talk to law students in Arizona about the enforcement of agreements between cohabitants? What if all of these students could converse about the law’s response to domestic violence? These were some of the questions that family law professors tossed around at a curriculum development workshop sponsored by the North American Consortium of Legal Education (NACLE) in 2001. That first discussion led to the development of an experimental cross-border course which has been offered to American, Canadian, and Mexican law students since 2003. This article describes the development of the NACLE Family Law Module. By focusing on the experience of students and teachers during the three semesters in which the NACLE cross-border course has been offered, we will bring out the pedagogical value of cross-border teaching for law students and faculty, and examine the benefits and challenges of teaching across national boundaries. Finally, the review of our experience helps identify some implications for future iterations of the course.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".