Effects of Canada’s Increasing Linguistic and Cultural Diversity on Educational Policy, Programming and Pedagogy
Bibliographic record
Abstract
In Canada, 22.9% of people report a “mother tongue” that is not English or French (Government of Canada, 2017) and most of them are newcomers. Within Canadian primary and secondary school, there were 4.75 million students enrolled in the 2015/2016 school year (Statista, 2018), and 2.2 million children under age 15 who were foreign-born or who had at least one foreign-born parent (Government of Canada, 2017). Thirty-seven and a half percent of all Canadian children have an immigrant background (Government of Canada, 2017). These statistics point towards large numbers of students in Canadian schools who have a depth of linguistic resources and repertoire. This diversity has implications on educational policy, programming, and pedagogy. In order to ensure that the education provided to students in Canadian classrooms is relevant, future-focused, and honouring to the depth of linguistic and cultural resources represented within the classroom it is necessary for teachers and policy-makers to have a strong understanding of how English as an additional language (EAL) students learn language and literacy, and how they enrich the learning environment of the classroom as a whole. This article describes the effects of Canada’s increasing diversity on educational policy, programing and pedagogy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".