Deep care: The COVID‐19 pandemic and the work of marginal feminist organizing in India
Bibliographic record
Abstract
Abstract In this paper, we adopt a Southern feminist epistemology to critically appraise the ways in which media discourse on gendered organizing during the Indian COVID‐19‐induced migrant crisis resists or reinforces hegemonic caste hierarchies. To contextualize this work, we briefly historicize scholarship on feminist organizing around land rights, hunger, and violence, while noting the politics of contagion and pollution narratives plaguing the pandemic discourse in India. After conducting a qualitative content analysis (QCA) followed by a critical discourse analysis (CDA) of media discourses across three tiers (international, national, and local), we found that international and national tiers of discourse largely deployed a savarna gaze that worked to 1) Reinforce brahminical and technocratic pandemic narratives and 2) Delegitimize Dalit marginal organizing feminist work and Dalit sensibilities through seven overlapping metrics of erasure. On the other hand, local tier of discourse confronted the savarna gaze, amplified voices of Dalit and Muslim women by centering their narratives of resistance, and tackled the exacerbation of casteist oppression under the pandemic in the service of emancipation. Local discourses also highlight how marginal organizing during the first pandemic lockdown involved provision of essential resources and services (food, medical care, security) for mostly Dalit and Muslim migrant workers, and women intersectionally facing domestic violence and savarna violence. Despite the brahmininal structural oppression, Dalit feminist praxis' emblematic resistance of oppressive structures, during and beyond times of crisis, constitutes what we call the work of deep care .
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.058 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".