Bibliographic record
Abstract
Dans leur introduction au livreLaw in the Domains of Culture, Austin Sarat et Thomas Kearns écrivent : «Law and legal studies are relative latecomers to cultural studies. To examine [law in the domains of culture] has been, until recently, a kind of scholarly transgression». L'inverse est vrai aussi : lescultural studies(incluant l'anthropologie) sont arrivées tardivement au droit et aux réflexions sur le droit, alors qu'on assistait, ces dernières décennies, à une irruption remarquable de discours culturels dans le domaine du droit. Il semble bien que l'acquisition d'une certaine «compétence culturelle» soit devenue obligatoire dans les cercles juridiques. Il n'y a pas que la floraison de séminaires et cours sur la «sensibilité culturelle» pour juges, avocats et policiers, mais le «concept de culture» traverse maintenant bien des décisions judiciaires en matière de droits autochtones, et la «défense culturelle» (bien que fortement contestée par certains et toujours sans approbation officielle) est désormais une dimension de nombreux procès criminels impliquant des immigrants. Aussi, laCharte canadienne des droits et libertésréfère à «l'héritage multicultural des Canadiens» (art. 27) dont la préservation et la promotion est une condition de sa propre interprétation.
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 | 0.001 | 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 | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".