Research as knowledge democratization, mobilization and social action: pushing back on casteism in contexts of caste humiliation and social reproduction in schools in India
Bibliographic record
Abstract
Dalit (the ‘downtrodden’) students continue to experience caste-based discrimination, humiliation and dehumanization; illegal practices that are being reproduced in the school system in the state of Odisha, India. Based on a research study organized by the Center for Research and Development Solidarity, an adivasi (original dweller/Scheduled Tribe)-dalit (Scheduled Caste) research organization and 401 dalit students in grades 6–10 attending 16 government schools in a 25-village zone, this paper elaborates on this research initiative. It demonstrates how knowledge democratization, both, as research undertaken with and for dalit students as producers of (caste-resistance) knowledge and as knowledge sharing as mobilization, can simultaneously mobilize wider circles of organized collective action with parents, Village Education Committees (VECs) and local dalit NGOs and movements to address casteism and untouchability in state schools. The paper concludes with some brief insights pertaining to academic and funded research as knowledge democracy and mobilization for social action that are emergent from this caste research and related research and social action addressing land-forest-labour assertions in South Odisha.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".