Projit Mukharji, <i>Brown Skin, White Coats: Race Science in India, 1920-66</i>
Bibliographic record
Abstract
Brown Skins, White Coats offers a richly detailed, gripping and largely overlooked history of the mobilization of racial thinking and of race science in anticolonial movements and postcolonial nationalisms. Urging a more capacious understanding of race science that goes beyond the more circumscribed and highly critiqued nineteenth-century manifestation, Mukharji uncovers a history of ‘seroanthropology,’ a field of inquiry premised on the assumption that ‘the frequencies of various serological factors’, such as blood groups, ‘varied by race’. (9) Not quite a discipline in its own right, but more than a mere technique for apprehending the truth of race, seroanthropology appears in this book as the forgotten bridge between the now-reviled nineteenth-century sciences of human difference, and the still entirely respectable study of human genetics. A sweeping introduction provides the still-needed reminder that the couching of various race sciences as pseudoscience rests on a series of faulty assumptions: that there is a clear demarcation between science and what masquerades as science; that biologically based racial thinking has been relegated to the past by scientists today; and that practitioners of race science were rabid racists whose bad politics bled into their work. As the book makes explicit, just as race science was not necessarily carried out by racists, normal science was not aligned, intrinsically, with social justice and other progressive ideals. Brown Skins boldly unearths the race research conducted in the name of a secular nation and its scientific understanding in the decades after India's independence. In doing so, the book forces us to confront the racial thought and frankly racist assumptions about human difference that persist in India today, even within secular, scientifc circles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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".