Multicultural Education in Canadian Preservice Programs: Teacher Candidates' Perspectives
Bibliographic record
Abstract
This report examines Canadian teacher candidates' perspectives on the multicultural education component of the preservice teacher education program they attend. The data analyze were collected through a questionnaire that set out to explore whether teacher candidates were satisfied with the multicultural education preparation they received and their ideas on how it might be improved. We present the findings in two parts. First we consider the teacher candidates' critiques and suggestions on multicultural education to reveal that the majority of respondents did not feel adequately prepared to the challenge teaching in ethno-racially diverse classrooms. Their responses point to specific programmatic and structural shortcomings of current multicultural curricula in Canadian teacher education programs. They suggest ways to improve the multicultural education curriculum including compulsory courses, more diversity among faculty and teacher candidates in the program, and an integrative approach across the teacher education curriculum. We then analyze the discourses embedded in their responses to examine how multicultural education is being understood by teacher candidates. Here we show that common understandings are often articulated through the paradigm of difference, through which the problems and the solutions are believed to be about the Other. We argue that these understandings promote rather than disrupt practices that sustain white hegemony. The article concludes with a discussion of the practical implications.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.007 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".