Le mélange de langues dans le discours d’enseignants marocains de français au Maroc, en dehors de la classe
Bibliographic record
Abstract
À travers cet article, nous nous sommes intéressé à une des conséquences directes des langues en contact, à savoir celle du mélange de langues : français-arabe chez des enseignants marocains du français au Maroc en dehors de la classe. À l’observation d’un corpus oral recueilli auprès d’un groupe d’enseignants de sexe masculin, nous nous sommes aperçus de la mixité des codes dans leurs discussions, une mixité qui se caractérise par le mélange de deux codes linguistiques totalement opposés. L’analyse que nous avons développée est orientée vers la composante lexicale à travers laquelle nous avons essayé de mettre à jour les facteurs susceptibles de provoquer le mélange des deux langues en contact. Cette analyse nous amène à conclure que même des enseignants — arabophones — de la langue française n’échappent pas aux mélanges de langues français-arabe.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".