Dual Language Books Go Digital: Storybooks Canada in French Immersion Schools and Homes
Bibliographic record
Abstract
In response to Canada’s growing ethnic, cultural, and linguistic diversity, educators in French immersion classrooms are increasingly responding with enhanced cross-linguistic initiatives, and dual language books are promising resources in the promotion of multilingualism (Zaidi, 2020; Zaidi & Dooley, 2021). This paper details a research project we completed in a 2019 classroom-based qualitative case study in a French immersion school experiencing an increasing enrollment of linguistically diverse students. The researchers sought to determine if Storybooks Canada, a free digital platform with 40 dual language books in multiple languages, could help promote literacy engagement and strengthen home-school connections. Five teacher participants identified a range of features that make the platform a useful resource for promoting literacy engagement, text comprehension, learner autonomy, meaning-making, and instructional differentiation. These included (i) the multilingual features, (ii) the ability to project stories on a large screen, (iii) the audio component, (iv) the illustrations, and (v) the different levels of text difficulty. While teachers made almost exclusive use of the French language features of the site, for classroom purposes, they supported cross-linguistic uses of the platform in the home context, with a view of strengthening home-school connections.
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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 | 0.001 | 0.003 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".