Aménagement de l’acquisition: du trilinguisme fonctionnel à la pédagogie convergente
Bibliographic record
Abstract
Social bilingualism, unlike official bilingualism, is very common. In most countries, the official language/s and tens or hundreds of other languages coexist; many such unofficial languages facing extinction. What is noticeable is that when language planning does not follow the ecological approach, i.e., when it emphasizes the strengthening of a particular language rather than the “structured diversity” of all the languages that make up a particular linguistic ecosystem, that can negatively impact the survival of minority languages. This study, which was carried out from the perspective of ecolinguistics, was aimed at promoting linguistic diversity through the protection of minority languages. It was essentially based on acquisition planning. The protection referred to here could be ensured, among other means, through the progressive acquisition of three or more languages in the education system. In Cameroon, a French – English bilingual country, the minority official language and many local languages are taught in school with varying degrees of success. It was interesting to critically look at some teaching approaches of those languages with the objective of showing how it could be possible, for those whose first language is neither French nor English, to better learn French and/or English, through convergent pedagogy, an educative approach based on the development of bilingualism or multilingualism. DOI: http://dx.doi.org/10.11591/ijere.v2i2.2021
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".