Bibliographic record
Abstract
This paper is an adaptation of the Tucker Lecture that I delivered in October of 2017. Its title depicts two iconic places, one in the Canadian province of Quebec, from where I hail, and the other in Louisiana, the locale of my audience. In this paper, I attempt to guide an allegorical voyage from la Beauce to le Bayou, from Quebec to Louisiana, from Montreal to Baton Rouge, from McGill to LSU, using a transsystemic itinerary. This voyage will showcase the unique way of teaching and thinking about law that has defined the program of legal education, and the imaginations of legal scholars, at McGill’s Faculty of Law for almost two decades. In addition to demystifying the elusive term “transsystemic,” and outlining the pedagogical and intellectual benefits of teaching and thinking about law in this way, this paper will focus on the increasing relevance of the transsystemic approach as a way of preparing jurists, wherever they may be, for the complexity and novelty of contemporary legal practice. By instilling creative, critical and flexible thinking skills, it enables jurists to deal with novel legal problems, to be more adept at envisaging a multiplicity of creative ways to solve legal problems through alternative methods of dispute resolution, and to keep pace with novel comparative judicial methodology. Just as la Beauce and le Bayou are different places with different geographical features, so too are Quebec and Louisiana different legal jurisdictions. However, they are, in many ways, sister jurisdictions, sharing a common mixity in their legal systems. This makes law schools in Louisiana a particularly fertile environment in which to showcase this unique itinerary in the hope that some of you will come along on this interesting voyage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 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".