Brown Identities, Complicities, and Complexities: Towards Brown-Black Solidarities
Bibliographic record
Abstract
Complicities and complexities in racialization simultaneously create possibilities and foreclose opportunities for cross-racial solidarities. In this auto-ethnographic paper, we share our learning and theorizing of Brown-Black solidarities since we filmed a webinar titled, “Brown Complicity in White Supremacy: Towards Solidarity for Black Lives” (SultyDee, 2020), one of several efforts towards Brown (South Asian)-Black solidarities in North America. We begin by situating ourselves in this conversation by troubling conceptions of “Brown” that flatten power asymmetries within the diaspora. We then theorize the inevitable complexities and complicities of the relational racialization of Brownness from anti-racist and anti-colonial framings and explore conceptions of Brown-Black solidarities. Finally, we draw on webinar feedback and our ongoing work in education and in communities in the Greater Toronto Area to offer a framework for strengthening Brown anti-racist orientations towards Brown-Black solidarities. This framework intends to examine intersections between white supremacy, anti-Black racism, caste oppression, and settler colonialism in a white settler state.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".