The punitive gap: NRC, due process and denationalisation politics in India’s Assam
Bibliographic record
Abstract
Abstract The creation of the National Register of Citizens (NRC) in Assam is indicative of the sharpening tensions surrounding citizenship, belonging and integration in India. Officially aimed at demarcating the “legitimate citizens”, its implementation is believed to have resulted in the partial exclusion of the so-called “Doubtful Voters” and denationalisation of the “illegitimate residents”. These frictions associated with citizenship identity and rights are nowhere as acute as in the northeastern Indian state of Assam, where measures of retroactive revocation, administrative erasure and withdrawal of citizenship rights have been systematically deployed against religious and linguistic minorities. Using new research with some NRC rejected applicants in western Assam and other materials, this article identifies the central aspects of the implementation gap in the crucial, albeit problematic task of locating the rightful “Assamese-Indian” citizens. Linking our work to the idea of the ‘process is the punishment’, we conceptualise these conspicuous inconsistencies in the NRC citizenship determination processes and their results as the “punitive gap”. We have identified the distinctive contours of this gap in terms of the massive economic costs, intensification of social (including gender and religion-based) inequalities, increased control through social suspicion and unpredictable outcomes for the marginal Miya Muslim community. The article highlights how this punitive gap has constantly eroded key components of due process, of procedural and substantive protections of the rights of individuals, during the NRC determination exercise and after the release of the final draft list.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.007 |
| 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".