Digital identity, datafication and social justice: understanding Aadhaar use among informal workers in south India
Bibliographic record
Abstract
Aadhaar, India's national biometric digital identity program aims to provide 12-digit number for every Indian resident. Through this Aadhaar seeks to achieve digital financial inclusion of groups like marginalized informal workers. This paper focuses on experiences of informal worker groups – of cab-drivers and domestic workers in a south Indian city who use Aadhaar as an identity for verification on online recruitment portals and gig-economy apps. The paper contributes a novel theoretical lens to the literature on ‘data justice’ and more broadly to ICT4D research. It operationalizes the cultural, economic and political dimensions of ‘abnormal justice’ as being synergistic with surveillance and datafication inherent to digital identity. Using empirical evidence of semi-structured interviews and field observations, this paper present three critical findings: current use of digital identities reifies extant cultural inequalities experienced by marginalized workers; unprotected datafication exploits the new-found digital participation of the marginalized to create further economic inequalities; and unfair and complex barriers continue to exist for the marginalized using digital identity to voice ‘informed consent’ or to access redressals to security issues.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".