Bilingual higher education in the legal context : group rights, state policies and globalisation
Bibliographic record
Abstract
Notes on Contributors Acknowledgments Introduction: 1. Legal education in bilingual contexts: A conceptual, historical and comparative perspective Xabier Arzoz Part I Legal Education in Multilingual States: 2. Bilingualism and legal education in Canada: The classical approach Andre Braen 3. Linguistic Law in Higher Education in Belgium: new trends for bilingual education, but which one? Sophie Weerts 4. The Swiss paradox: Monolingual higher education in a multicultural environment Nicole Schmitt 5. Implementing linguistic rights through legal education in Finnish and Swedish in Finland Markku Suksi Part II Legal Education through Minority Languages: 6.Basque-medium legal education in the Basque Country Xabier Arzoz 7. Bilingual higher education in Catalonia Eva Pons 8. Living on borrowed time: Bilingual law teaching in Galicia, or the urgent need to recover prestige Alba Nogueira 9. Bilingual legal scholarship in Wales: historical and contemporary perspectives Gwyn Parry 10. Legal education in Hungarian language in Transylvania: Between a glorious past and an uncertain future Gyula Fabian 11. Creating, studying and experimenting bilingual law in South Tyrol: Lost in interpretation? Elisabeth Alber and Francesco Palermo Part III The emergence of English as a language of legal education: 12. English-medium legal education in continental Europe: Maastricht University's European Law School - Experiences and challenges Nicole Kornet Part IV Conclusions: 13. Bilingual legal education in Europe and Canada Bethan Sarah Davies Index.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".