Bibliographic record
Abstract
Perhaps underestimated, however, is how particularly crucial the role of legal education becomes where the legal systems concemed fall into the category of so-called "mixed jurisdictions."lFor in such systems, legal players must be capable of playing two games at once, which requires that they be trained to juggle with, and yet never confuse, two distinct sets of rules.Only if legal players can properly accomplish this will the integrity of the various games being played be preserved.In mixed jurisdictions, therefore, it is the very identity of the legal games, not just their respective dynamism, that is at stake for legal education.As one such mixed jurisdiction, Quebec is faced with a singularly onerous educational challenge.Because all matters of private law are governed in Quebec by a system of rules rooted in the continental tradition of civil law,2 law students in Quebec must be trained as civilian jurists.At the same time, Quebec's membership in the Canadian federation3 entails its endorsement of the Anelo-Saxon tradition of 1. Trrg RoLE or Juorcru-DrcIsloNs ,arNp Docrnwe N CryL Law e,No nl MxED
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 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".