Language Situation and Language Policy in the Canadian Province of New Brunswick
Bibliographic record
Abstract
The analysis of sociolinguistic situation in the Canadian province of New Brunswick is offered in the article. The history of the settlement of this territory by representatives of different linguistic cultures — the French and the British — is considered. An overview of the demo linguistic situation in the province is given. The statistical data of the latest population censuses are presented. Particular attention is paid to the use of the minority French language in various social and communicative spheres in New Brunswick at the present stage: in the legislative and executive branches, in the main sphere of the language functioning — in the sphere of education, in the spheres of services, trade and the media. The author dwells on the problem of variation of the Acadian French language in a situation of institutional bilingualism, when the French language is constantly under the influence of the dominant English language. The relevance of the article is due to the attention of the Russian and world community to the position of minority languages in a multilingual society and the problem of their preservation. The novelty of the research is seen in the fact that the ongoing language policy is considered simultaneously with the analysis of existing laws on language, since only adopted laws can allow members of the linguistic minority to assert and defend their rights.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".