Enabling Translanguaging in the French Language Classroom
Bibliographic record
Abstract
Recent studies in multilingual and translanguaging pedagogies have shifted the focus from investigating how students engage their multilingual repertoires to exploring how teachers understand and implement these pedagogical directions in their practice. In this article, the authors report on a national online survey on the multilingual perspectives and practices of teachers of French in Australia. The overall goal of the survey discussed here was to comprehensively capture how teachers of French understand the teaching and learning of languages in general, and of French in particular. The study revealed several tensions between the language teachers’ beliefs and practice. While most of the survey participants expressed strong support for innovative pedagogies such as translanguaging (García & Wei, 2014), and keen motivation to engage the full multilingual repertoire of their learners, a closer reading of the data indicated that most of them felt restricted in their practice by “the normative terms and conditions of an understanding of languages education that remains rooted in parochial, monolingual and pecuniary perspectives” (Weinmann & Arber, 2017, p. 173). In particular, the findings indicate that (self-)perceptions of “non-native” language teachers as “culturally deficient” continue to frame the notion of what constitutes a “good” language teacher (Holliday, 2015). For teachers to feel more confident and better equipped to effectively implement translanguaging pedagogies in their practice, teachers’ perceptions of their own multilingual identities and how these are shaped within the systems they work in (Young, 2017) need to be better understood. Keywords: Languages teaching, languages education, translanguaging, native language teacher, non-native language teacher, linguistic repertoire, multilingualism, Australia
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".