Bibliographic record
Abstract
At the heart of this inquiry are eight Montreal educators and their teaching experiences in multicultural schools and classrooms. These female and male practitioners represent different racial, ethnic, linguistic and class groups, and a range of teaching experience. This study considers how their understanding of diversity shapes and informs their teaching practices in the specific socio-political, cultural and linguistic venue of Montréal, Quebec. My theoretical framework brings together the work of established scholarship, and examines contributions to the issues of language, culture, and identity, as they impact multicultural education and the learning and teaching environment in schools. This qualitative inquiry is guided by my own multicultural awareness as a bicultural Canadian and an educational researcher. I use case study methodology as a form of inquiry within qualitative research, collecting field notes from various sources, and searching for stories that provide insight into the educators' wisdom and practical knowledge. The teachers' stories reveal the knowledge, attitudes, skills, awareness, and values that contribute to dialoguing across difference, and provide us with an image of what a multicultural educator might look like. The overarching theme of this study is the potential fusion between educational theory and classroom practice. The educators profiled offer a framework for communicating and relating to pluralistic learning and teaching environments, and open the possibility for meaningful dialogue about diversity in classrooms, schools, and the community at large. In affirming the harmony of cultures in their schools, the teachers breathe life into new multicultural spaces where cultures intersect, and where new forms of understanding are created. Several themes emerge in this study: the educators' changing role in a multicultural teaching environment, dialoguing with ‘other’, and the rewards and challenges of providing equitable educational opportunities for all students. Given the shifting racial and cultural composition of Canada's population, education must prepare students and teachers for life in a multicultural society. Teacher education programs must evolve to address the needs of changing racial and cultural student populations, and engage teachers in learning experiences to enhance their effectiveness in multicultural classrooms. The study concludes with recommendations for integrating multicultural perspectives in teacher education.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".