Language Policy in Education: Second Official Language in (Technical) Education in Canada and Cameroon
Bibliographic record
Abstract
It should be said from the outset that, apart from Esambe’s (1999) MA thesis, no comparative research, to the best of our knowledge, has so far been devoted to the study of language policy in education in both Canada and Cameroon. Yet, these two countries offer a fascinating basis for comparison because English and French (which were instituted at roughly the same time in these two countries) are the two official languages in each country, but the minority status is reversed. This study, which rests on the observation that students from technical training programmes generally underperform or lack interest in their second official language (SOL), aims at comparing the current policies of SOL in education in order to see how both countries’ experiences can be mutually informing. To achieve its purpose, this research focuses on the analysis of the policies of official languages (OLs) in education in both countries, specifically regarding technical training programmes. More clearly, language policy in education and SOL education policy as obtained in both countries will be comparatively examined. The comparison, it is hoped, will reveal the fundamental causes of the overall poor performance or lack of interest observed in Cameroon and Canada respectively.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".