Surveillance in the Name of Governance: Aadhaar as a Fix for Leaking Systems in India
Bibliographic record
Abstract
Abstract Many jurisdictions are employing biometric technologies to collect data about and verify the identities of social assistance recipients, with fraud prevention and cost savings serving as common justifications for doing so. This chapter explores the practices of building the infrastructure to monitor welfare beneficiaries, many of whom are vulnerable or marginalised populations. To do so, the chapter examines the Aadhaar system in India, which has issued over one billion unique identification numbers since being launched in 2010. The analysis illustrates a one-way expectation of knowledge and transparency (i.e., for citizens to disclose in order to access services), drawing attention to how nationalist agendas and forms of inequality inform who is subject to the state’s terms and conditions. In doing so, it considers how these forms of surveillance evince broader shifts in which state and non-state actors rely on knowledge to regulate subjects.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".