Bibliographic record
Abstract
This thesis examines the possibilities and limitations of theorizing Brown identity as an anti-racist and anti-colonial framework.By examining discursive representations of Brownness and Brown Identity in the Brown Canada Project, a community-led project of the Council of Agencies Serving South Asians, it introduces a new framework for conceptualizing the racialization, identity, and resistance of South Asians in the Greater Toronto Area.The thesis reveals three key themes: the salience of Brown identity in terms of a spirit injury that results from migration, assertion of pride in resistance, and how shared values and experiences of racism form pedagogies for education and community-building.These themes inform a theory of Brown identity and Brownness for anti-racist and anti-colonial resistance.This thesis aims to inform anti-racist and anti-colonial educational practices, political activism, and social movements.It serves as a point of generation for new lines of inquiry into Brown epistemologies, experiences, and relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.118 | 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 teacher head, 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".