Bibliographic record
Abstract
Abstract Caste, and caste-based discrimination, are not just Indian issues. They are experienced throughout the world, from Britain to Bahrain, Canada to South Africa. This is a global phenomenon, demanding global solutions.Leading scholar Suraj Milind Yengde shines a light on the Dalit experience internationally, from indentured labourers in the nineteenth-century Caribbean to present-day migrant workers in the Middle East. Combining history, ethnography and archival research, he offers a compelling, comparative approach to caste and race from ancient times to today. What have been the impacts of colonialism, religion and nationalism on caste-based hierarchies worldwide? What can we learn from caste-related movements in India and internationally? Why hasn’t the South Asian diaspora embraced the anti-caste struggles of the homeland? And what are the limits of Dalit–Black solidarity?Exploring the global footprint of the anti-caste struggle—from its links with Black Lives Matter to the work of international Ambedkarite organisations—this is a powerful analysis of world politics from the perspective of one of the most oppressed communities on Earth. Asking probing questions about the nature of inequality, Yengde issues an energetic call for a cosmopolitan Dalit universalism, as a vital part of today’s fight for social justice and equality.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.021 |
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".