What Does it Mean to be a Solution?: Teachers of Colour Doing Social Justice in the Multicultural School in Canada
Bibliographic record
Abstract
This project conceptualizes the racialized teacher subject in the multicultural Canadian school in Toronto, Ontario, by examining what it means for the Black, Indigenous and teacher of colour to be positioned to solve inequity and racial injustice in schools. It asks: What does it mean for the racialized teacher to be a solution? To address this overarching question, the project examines the following issues: how racialized teacher subjectivity becomes constituted prior to and while working in the multicultural school; what kinds of expectations the racialized teacher subject navigates; and the systems that function to produce and constrain the racialized teacher’s social justice goals. Because the project is interested in the complexity of the racialized teacher in a white settler state such as Canada, the space of the racially diverse or “multicultural” Toronto area high school was the site of investigation. The core argument presented is that the racialized teacher subject is restricted through liberal and neoliberal discourses, contending with the position of what I term the multicultural helper – an idealized figure who is expected to facilitate the inclusion of the racialized student into the multicultural school. The types of solutions racialized teachers are typically invited to take up are those that model and facilitate depoliticized and culturally acceptable subjectivities for their students. I argue that racialized teachers do not and cannot actually embody social justice; rather they struggle against and negotiate the position of the multicultural helper. Crucially, they unlearn dominant notions of Canadian multiculturalism in the production of a politically conscious subjectivity. I further argue that the liberal expectations inscribed in multicultural helping require racialized teachers to navigate the contradictions between the benefits and harms of the ways they are positioned in schools. I illustrate that spaces and systems within schooling, in particular those outside the direct influence of the racialized teacher, are governed by discourses of control (Nolan, 2011) and discipline (Foucault, 1995), reproducing injustice and limiting the transformative potential of the racialized teacher’s work.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.071 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".