Multiculturalism, Diversity, and Education in the Canadian Context: The Search for an Inclusive Pedagogy
Bibliographic record
Abstract
The Canadian Multicultural Policy, introduced by the federal government in 1971, established a framework that, over the years, has informed the ways in which educational institutions have come to recognize the cultural diversity of the Canadian population, and has initiated educational programs that address issues related to race, national and ethnic origin, color, and religion. Although some educators and writers claim that multicultural education as implemented in schools has been responsive to the needs, interests, and aspirations of the diverse population of students (Mansfield & Kehoe, 1994; Samuda & Kong, 1986), critics have argued that this approach to education has been limited in its capacity to do so particularly with regard to marginalized students. To support their point, the critics pointed to the situation where, even with the implementation of “multicultural education,” low teacher expectations continue to contribute to the streaming of minority and immigrant students into low-level educational programs, resulting in alienation and high dropout rates (Cummins, 1997; Curtis, Livingstone, & Smaller, 1992; Dei, Muzza, Mclsaac, & Zine, 1998; Lucas & Schecter, 1992). On this basis, multicultural education as practiced has been unable to ensure equality of educational opportunities and, still less, equity for all students within the Canadian education system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".