Negotiating "Brown": Youth Identity Formations in the Greater Toronto Area
Bibliographic record
Abstract
This study problematizes the notion of a Canadian identity that is constructed within the framework of liberal multiculturalism. The primary goal of this study is to explore how a racial identity of "Brown" is conceptualized, expressed, and negotiated in the Euro- centered White space of schools. To do this, I interview five 1.5-generation immigrant youth from the Greater Toronto Area who self-identify as "Brown."This study finds that "Brown" is a fluid, multi-layered identity that is expressed differently in different contexts. Further, "Brown" youth use the identifier of "Brown" as a way to make space for their identities because "Brown" experiences are silenced in the Black/White binary in which racial conversations take place. Moreover, this study finds that although "Brown" youth encounter racism in school, they rationalize these acts. Finally, "Brown" youth invoke the hyphenated identities of "Brown-Canadian" and "South Asian-Canadian" to navigate the contradiction of being "Brown" and being "Canadian."
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".