Multilingual education in practice : using diversity as a resource
Bibliographic record
Abstract
0 This book presents outcomes of Canadian school-communityuniversity collaboration (p. ix). ESL students constitute valuable resources, and heritage languages are seen as vehicles for successful language acquisition. The first chapter presents framework for academic language learning, which is applied in chapter 3. Chapter 2 outlines inclusive (p. 17) reception newcomers receive at Thornwood Public School, offering ideas that schools with fewer ESL teachers may need to adapt. Chapter 3 shows how to effectively use students' sociolinguistic capital. Chapter 4 analyzes ESL student writing. Chapter 5 discusses how teacher training programs can better serve multilingual students. The final chapter addresses administrative and professional development issues. Current research prioritizes home-school collaboration, aimed at improving academic success of language learners, but studies rarely focus on how of this endeavor. This timely text is one of few. The few unclear segments include failure to contextualize language learners and statements such as following: They [students] are expected to develop toward native-like competence (p. 20). One wonders, for instance, why students from Outer Circle (Kachru, 1982), such as Abhinaya, from Sri Lanka, are categorized as ESL students. Abhinaya's mother, who remains nameless, is described as using halting English (p. 17), and Ambi, a fluent speaker of Hindi and Urdu (p. 25), translates for the family (p. 22), when, in all likelihood, they are more fluent in Tamil and Singhalese. In fact, it is unclear why translator is necessary. Another example of potentially mistaken proficiency is Myra, from Pakistan, whose writing reveals some knowl-
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| 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".