Diverse Classrooms, Diverse Teachers: Representing Cultural Diversity in the Teaching Profession and Implications for Pre-Service Admissions
Bibliographic record
Abstract
This article challenges the homogeneity of the teaching profession in Canada by articulating the pressing need for a more diverse teaching body, as it relates to students’ academic achievement and social well being. Given the subject location and interest of the author, literature reviewed in this paper primarily revolves around research on Black educators. The essential themes that underscore this paper are: teachers of colour as role models, culturally relevant pedagogy, and pedagogies of Black teachers. While there is a growing body of literature on teachers of colour in the American context, there still exists a paucity of Canadian research on teacher diversity, and the ways in which pre-service programs are implicated in the recruitment and retention of racialized teacher candidates. Using an anti-racist lens, I examine the role of Black educators within the nexus of representation and pedagogical diversity. Finally, I elucidate the integral role of Faculties of Education in responding to an equitable representation of Canadian teachers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.047 | 0.025 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.023 |
| 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".