Theorizing the Local: Diversity, Race and Belonging in the City of Toronto
Bibliographic record
Abstract
This thesis engages with critical race and postcolonial theories to explore how race is reproduced and organized through diversity discourse in the City of Toronto. The central question of my research is: which historical conditions and practices, tied to what kind of truth-claims, are re-articulated and justified by diversity discourse? The focus of this study is an examination of how power is negotiated and transformed through multiple conceptual and embodied schisms into the re-production and justification of particular truths which, in turn, provide conditions for the possibility of diversity discourse in the present. My research involves two phases: interviews with 15 racialized City of Toronto staff to explore their multiple positionings in the active subjectivization and instrumentation of diversity discourse, and a detailed genealogical review of past and present diversity-related documents from the City of Toronto to expose the illegitimate accounts of diversity discourse. In this second phase, I begin to reflect diversity in the City as a series of events, bending to the will of political and racial forces and their effects. I draw and expand upon critical discourse analysis to analyze how the uses and understandings of diversity by racialized staff get taken up and reproduced through formal City documents. This helps me to outline the complex conditions of power and resistance, and how they are negotiated by racialized City of Toronto staff in and through the institution. \n \nIn my analyses, I demonstrate how diversity discourse limits the belonging of racial Others in the City of Toronto, whereby they become articulate(d) subjects only to the extent that diversity is reiterated, reproduced, and cited by them, and through them. I also explore how diversity discourse invites negotiations of belonging via being bound with deeply affective longings to be not-strange, not-raced, with the understanding that the various subjectivities that are caught up in processes of yearning are reproduced through diversity discourse as racialized subjects. Finally, in my conclusion I attend to the ideas of complicity, contradiction and refusal in diversity work, as mechanisms of disruption. I also reveal my own complex and produced positionings in this diversity work, as one who seemingly stands outside the research, in order to expose my own complicities in the very violence which I seek to make visible and disrupt.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.049 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".