CAA-NRC-NPR, intra- and cross-movement solidarity, and trans and queer resistance in India
Bibliographic record
Abstract
In 2019, widespread protests against CAA-NRC-NPR in India witnessed the coming together of variously minoritized, historically oppressed peoples in resistance as the threat of disenfranchisement stared them in the face anew. State-wide demonstrations offered resistance against majoritarian and exclusionary legislative enactments in the form of CAA-NRC-NPR that were seen as a veritable assault on the ‘precarious citizenship’ of Muslims, Dalits, Bahujans, Adivasis, as well as undocumented and document-precarious people in India – including trans and gender-variant people who are also India’s ‘precarious citizens’. Protests against CAA-NRC-NPR offered possibilities of cross-movement as well as intra-movement solidarities to flourish, illustrating the fact that collective liberation and justice can only be imagined and worked towards in a context where historically marginalized communities can enact resistance together against inequity, oppression, disenfranchisement, segregation, and mass dispossession. This viewpoint article explores how intra-movement and cross-movement solidarities, solidified during the anti-CAA-NRC-NPR protests, strengthened the movement led by ‘precarious citizens’ instead of derailing it, evidencing the fact that when organizing efforts honour overlapping interests, fears, and multiple historically marginalized identities, they are able to expand the horizons of justice, ethics, as well as collective liberation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".