Bilinguisme, politiques et attitudes linguistiques au Cameroun et au Canada
Bibliographic record
Abstract
Just like Canada, Cameroon is a bilingual country in which French and English are the official languages. But unlike in Canada, in Cameroon, the statistical minority is English-speaking while the statistical majority is French-speaking. A comparison of both sociolinguistic situations can lead to a better understanding of bilingualism (both as a social reality and as an individual practice) in both countries. This paper depicts the similarities and differences between the two sociolinguistic situations. It shows that the differences, which derive mostly from the differentiated relationship that Cameroonians and Canadians have with their official languages, determine the choices of language policies and influence attitudes towards individual bilingualism. Resume Comme le Canada, le Cameroun est un pays bilingue dans lequel le francais et l’anglais sont langues officielles. Mais contrairement au Canada, au Cameroun, la minorite statistique est anglophone tandis que la majorite est francophone. Le rapprochement des deux situations sociolinguistiques peut permettre de mieux comprendre la realite du bilinguisme (aussi bien en tant que realite sociale qu’en tant que pratique individuelle) dans les deux pays. La presente etude s’emploie a mettre en evidence les ressemblances et les differences entre les deux situations sociolinguistiques. Elle montre que les differences, qui decoulent pour l’essentiel du rapport differencie que les citoyens des deux pays ont avec leurs langues officielles, determinent les choix des politiques linguistiques et influencent les attitudes vis-a-vis du bilinguisme individuel.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".