Linguistic and Cultural Heterogeneity in the Classroom
Bibliographic record
Abstract
This chapter attempts to demonstrate the intrinsic interest of Canadian policy and practice concerning ethnolinguistic diversity in the classroom—but also its generalizable value in other settings. It contextualizes matters with a brief discussion of traditional and (slightly) more enlightened assimilative perspectives and pressures, and with an overview of official policies, practices, and perceptions of bilingualism and multiculturalism. It is suggested that there are two important types of language contact at school—that of dialects of the “mainstream” language, whose more standard form is usually the variety reinforced by the school itself, and that of different languages meeting in the classroom. It is also suggested that multiculturalism at school falls into two main categories—the first involves what ought to be general practice in the provision of intercultural sensitivity and awareness (“All education worthy of the name is multicultural”), while the second reflects the particular linguistic and cultural complexities existing beyond the school gates; these are not, of course, watertight compartments. Throughout, the emphasis here is on the need for multiculturalism-at-school to be a seamlessly integrated part of the educational enterprise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".