Bibliographic record
Abstract
Indians on Indian Lands studies dominant caste Indian diasporic formation within the Canadian settler state. Specifically, it theorizes Indian immigrant labor in resource extraction industries, logging, and canneries in unceded lands of British Columbia in the 1960s-1990s and the tar sands in Treaty 6 lands of Alberta currently. The book examines these sites as simultaneous spaces of Indigenous dispossession and spaces of racialized-classed-gendered-casted labor formations. In this book, I explore relationalities, intimacies, complicities, and solidarities of dominant caste Indian diasporic communities with Indigenous people in intertwined processes of settler colonialism, racial colonial capitalism, brahminism, hindu nationalism, anti-Blackness, and heteropatriarchy. It juxtaposes these messy complicities with solidarities as practiced by South Asian activists who are not dominant caste hindus in Tkarón:to. Weaving theory, interviews and conversations, ethnography, cultural and literary analysis, archival research, analysis of recent events, and secondary literature, the book forms the archive of Indigenous and Indian spatial and affective intimacies that exist within and across the empire. This multi-sited, multi-method, and interdisciplinary approach foregrounds stories and makes visible colonial intimacies, casted complicities, and other solidarities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".