Bibliographic record
Abstract
The law of languages truly is new ground for legal thinking and even may be considered futuristic in as much as it is law that recognizes differences among men. In this respect, the Loi sur la langue officielle and the Charte de la langue française of Québec confirm the right to specific linguistic expression in the form of acts that are territorially and materially exhaustive — these laws are outstanding examples for anyone who is interested in comparative law of languages. Nonetheless, the law of languages profoundly touches upon concepts that are of capital social importance : culture, minority language rights and fundamental freedoms. Furthermore, the very object of linguistic legislation which of course is language, is per se an object that hardly lands itself to appropriation either linguistically or legally — and as a basic means for expressing legal thought, language simultanously is the subject and object of law dealing with meta-legal and meta-linguistic concepts. Lastly, there exist in Québec important restrictions of both a structural and operative nature that relate to the interpretation and enforcement of Quebec law on language usage. This is why the legal impact of language laws, in general, and Quebec law, in particular, is of minor importance, whereas the cultural impact is of major concern.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".