Biometric data's colonial imaginaries continue in Aadhaar's minimal data
Bibliographic record
Abstract
Abstract This paper considers three moments in the treatment of data about race and identity in India. Many elements go into the development of data imaginaries as these change over time. A complete history is beyond the scope of this paper, but I develop three key episodes to explore critical but changing features of interrelations between race, identity and statistical arguments historically. One aim is to explore key features of the argument developed by two significant individuals – Thomas Nelson Annadale and P.C. Mahalanobis – as they sought to develop databases that could answer questions about race formation and, in the case of Mahalanobis, might also be used to develop statistical methods on the one hand and aid governance on the other hand. A second aim is to use this historically based but highly selective investigation to uncover key features of the ideology with which the government of India has presented Aadhaar, its vast biometric identification system powered by authentication technologies afforded by artificial intelligence. This enables me to identify different forms of racial or ethnic identity that could be – and in one or two cases actually have been – implicated in the way Aadhaar has been used in practice.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".