Bibliographic record
Abstract
Chapter 3 builds on the previous two chapters to bring together an anti-caste analysis with anti-settler colonial critiques to illustrate the intersections of race, caste, and Indigeneity. It argues that while caste may not be an obvious factor in the analysis of settler colonialism, any comprehensive understanding of Indian diasporas is incomplete without critiques of brahminism. Drawing upon my fieldwork in Fort McMurray, I argue that for dominant caste Indian hindus the Indigenous Other is constructed through colonial and caste processes that allow for the dominant caste Indian-self to recognize the latter as the caste Other. The subsequent discussion shifts the focus to the U.S. to illustrate the different ways hindu nationalist right-wing hindutva forces, an extension of brahminism, are organizing in the diaspora and how notions of Indigeneity are often invoked by these players drawing upon Indigenous frameworks from Turtle Island. In this chapter, I bring together caste and settler colonial technologies from Canada, U.S., and India to foreground the fact that brahminism is a transnational project. It is critical to understand how caste supremacy functions in tandem with other global projects of settler colonialism, anti-Muslim racism, white supremacy, fascism, and neoliberalism.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".