Examining Students’ Co-construction of Language Ideologies through Multimodal Text
Bibliographic record
Abstract
French immersion (FI), one of the hallmarks of French as a Second Language education in Canada and mandated in New Brunswick, Canada’s only officially English/French bilingual province, is often the target of language ideological debates surrounding its purposes and expected outcomes. Yet, notably absent in FI scholarship has been a focus on the ideologies informing students’ investment in French, including what bilingualism might mean for their language learning and identity. In this article, we discuss nine Grade 8 French immersion students’ co-construction of language ideologies regarding bilingualism. In a focus group, these students created a promotional video regarding the merits of bilingualism whose audience was comprised of fictional peers in a predominantly Anglophone province. Our analysis was guided by Darvin and Norton’s (2015) model of investment. We employed the tools of multimodal critical discourse analysis to consider the students’ construction of language ideologies through their video production. Through macro and micro analyses, we identified five primary ideologies: Bilingualism (a) is a matter of personal decision; (b) provides access to jobs; (c) provides access to economic capital; (d) provides access to Francophone communities of practice; and (e) provides access to symbolic capital. We discuss how the students have “remixed” the dominant provincial ideologies on bilingualism into their own, considering the implications of these ideologies on their investment in French. Finally, we suggest how multimodal practices provide a means to develop language students’ meta-cognition and expand their investment in their target language.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".