Should the Rainbow have Black and Brown Stripes?: (Anti)-Racism and Coalitions in Toronto’s Rainbow Community
Bibliographic record
Abstract
My dissertation research undertakes an in-depth analysis of queer (anti)-racism in Toronto’s rainbow community. My study is informed by ethnographic research methods, including semi-structured interviews with those who self-identify as part of the rainbow community, and my own observations at queer-focused events in Toronto such as the Greenspace Festival by the 519 and Pride Toronto’s annual parade. My analysis and findings are presented in three publishable articles. This portfolio-style dissertation manuscript also includes photographic images from my field work, and the script from my stage production We without You, an original theatre project that I created to reflect, analyse and disseminate my ethnographic research. In the first article I discuss how languages of capital accumulation, white (homo)nationalism and safety, are mobilized in order to produce race and racism in queer politics. Within contexts of intersectional tension I analyse how self-identified white participants conceptualize anti-racism allyship. In my second article I explore how conflict and confusion about appropriate allyship produced self-exclusion from anti-racism efforts. I found that allies experience tension as to their roles and potential responsibilities in relation to anti-racism, which often results in being uninformed about race-related issues and/or in their non-participation in anti-racism. With data and analysis from my first two articles I wrote and produced a stage production entitled We without You. The production is a knowledge mobilization project that addresses gaps in public knowledge and informs audiences about a range of experiences in the rainbow community shaped by intersections of race and queerness. My experiences writing and producing We without You and the role of performance as critical resistance against polarizing politics is the subject of my third article. I argue that performance as knowledge mobilization plays a crucial role in bridging academic work with public engagement by positioning sociological inquiry as an important contributor to public life. As a whole, this dissertation explores how race is produced in the rainbow community, examines the tensions involved with anti-racism allyship, and demonstrates the power of performance as critical resistance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".