Linguistic And Cultural Variation In The K12 French Classroom: The Role Of Canadian French
Bibliographic record
Abstract
The Francophone world is a vast landscape full of diverse people and cultures that share a common linguistic identity through the French Language. The world is ever-expanding with thousands of new learners. There exist many varieties of French; however, Canadian French is the most distinct variety. The present study sought to explore three essential questions: are teachers aware of the linguistic differences? Do they integrate media from Quebec into their classrooms? Lastly, how do they perceive sociolinguistic attitudes towards Canadian French? A full literature review was conducted to examine current research on the topic and to see where this study would fit in at. A mixed-methods survey was used with over 100 French teachers from various backgrounds across the southeastern United States. Also, after the survey was completed, a statistical analysis was conducted so that conclusions could be drawn about the three essential questions. Teachers were mostly aware of key differences between the two French varieties. They did tend not to integrate media from Quebec as often. Also, attitudes tended to be more neutral than at first thought. Knowing these answers can be used to inform pedagogical practices in the classroom and reshape the usage of authentic materials in additional language learning. Also, teachers can be more aware of how their attitudes could affect their student's perception of the target language and culture.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".