Bibliographic record
Abstract
The undergraduate law curriculum adopted at McGill University in 1998 - the transsystemic programme - was born of the unique political, social, and intellectual histories of its Faculty of Law. This essay reviews these contexts and characterizes the programme as an ongoing conversation about law, language, and knowledge that has animated the teaching programme since the faculty's founding, 150 years ago. The essay begins by juxtaposing the phrases No Vehicles in Park and No Toilets in Park to suggest that law and legal education are hermeneutic endeavours embedded in social experience. At McGill, this interpretive practice may be described as - a term the authors coin to capture the theoretical ground of transsystemic leaching, an epistemological and pedagogical practice at once pluralist, polycentric, non-positivist, and interactive. Using the first-year introductory course Foundations of Canadian Law as an illustration, the authors suggest new directions for the programme. They argue that one of the key goals of the transsystemic programme is to increase opportunities for students to become the agents of their own education and, concomitantly, to participate in the reconstruction of law and legal knowledge. The transsystemic programme challenges orthodox practices and established categories of knowledge. Curricular configurations, however, cannot be frozen: even constitutive polyjurality may one day lose its privileged place as an interpretive theme at McGill.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".