"People Like Me": Racialized Teachers and the Call for Community
Bibliographic record
Abstract
The city of Toronto is one of most racially diverse places in the world, with almost half of its population identifying as being a “visible minority” (Statistics Canada, 2010). As a result, the field of education faces the question of how to meet the needs of their transforming student demographics. Numerous researchers and institutional policies have responded to these changes by endorsing the hiring of a teaching staff that is reflective of the racially diversifying student population (Ontario Ministry of Education, 2009; Ryan, Pollock, & Antonelli, 2009; Solomon, & Levine-Rasky, 2003). The assumption, however, that racialized educators will automatically be effective teachers or role models for racialized students homogenizes their social differences and reduces the multiplicity of their identities to the colour of their skin (Martino, & Rezai-Rashti, 2012). What is urgently lacking from these dominant discourses are the voices of racialized individuals, whose inside perspectives and lived experiences can provide valuable insights about the roles of equity and race in education. Using an anti-racist theoretical framework to guide my research methodology, this study examines how racialized teachers understand their classroom practices, school relationships, and institutional policies with respect to race, equity, and the expectations that are cast to them as “visible minority” educators. A document analysis of educational statements that discuss race, equity, and anti-racism reveals that while policy has progressed, the presentation of these issues remains largely superficial and does not provide enough information or transparency to adequately communicate their importance. Nevertheless, the power of these dominant discourses has been vastly significant in shaping the lived experiences and feelings of racialized teachers, 21 of whom were individually interviewed using a qualitative, semi-structured method. The inside perspectives of these teachers demonstrate the complexity of race and its inadvertent impact on their roles as educators; their feelings and reactions illustrate the ongoing gap between policy and practice, the ignorance that is embedded in notions of racial matching between teachers-students, and the persevering call for a professional community where individual differences are viewed as opportunities to learn rather than obstacles that need to be overcome.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".