Bibliographic record
Abstract
Abstract ‘The metaphor of race is a dangerous weapon whether it is used for asserting white supremacy or for making demands on behalf of the disadvantaged groups...Treating caste as a form of race is politically mischievous; what is worse, it is scientifically nonsensical’. Andre ‘…what is in fact “scientifically nonsensical” is Professor Beteille’s misunderstanding of “race”. What is mischievous is his insistence that India’s system of ascribed system of social inequality should be exempted from the provisions of a UN Convention whose sole purpose is the extension of human rights to include freedom from all forms of discrimination and intolerance – and to which India, along with most other nations, has committed itself” Gerald Berreman (cited in ) ‘The possibility that the current Indian Hindu‐Muslim or upper versus lower‐caste conflict may be, in a significant sense, a variant of a modern problem of “ethnicity” or “race” is seldom entertained…”racism” is thought of as something the white people do to us. What Indians do to one another are variously described as “communalism”, “regionalism” and “casteism” but never “racism”’. Dipesh
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.071 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".