Storybooks Canada, English Language Learners, and the School Curriculum
Bibliographic record
Abstract
Given the large number of students from immigrant and refugee backgrounds in Canadian schools, our study investigates to what extent an open access multilingual digital platform, Storybooks Canada (https://www.storybookscanada.ca/), might serve the interests of elementary school English language learners. Our study drew on insights from 13 experienced language tutors across greater Vancouver, each volunteering for a local organization in an after-school program for multilingual learners. We sought to determine how the diverse stories on the Storybooks Canada platform could be used in classrooms and homes in British Columbia and Canada. We investigated a range of questions, including the following: Is Storybooks Canada a helpful resource to improve student reading? Can Storybooks Canada be used to build home/school partnerships? How can the stories be used within the British Columbia Curriculum? We then did a follow-up study of British Columbia’s English Language Arts curriculum in order to align the stories with curricular mandates. Our findings suggest that, given the universal themes of the stories, and the 18 languages available in text and audio, Storybooks Canada is a valuable tool for the maintenance of the first language, while supporting English language learning. Further, links between the stories and the British Columbia Curriculum may be helpful for teachers within and beyond British Columbia. We conclude with the hope that Storybooks Canada, and other derivative sites on the Global Storybooks portal (https://globalstorybooks.net), might support English language learners in Canada and the international community.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".