Unequal among equals: lessons from discourses on ‘Dalit Muslims’ in modern India
Bibliographic record
Abstract
Questioning ‘Dalit Muslims’ as an authentic social group, the authors enumerate the challenges inherent in presupposing that clearly delineated social groups exist and challenge the efficacy of designating such groups as discernible and cohesive. An interdisciplinary critique that draws on history, religion and social sciences, reveals a pervasive, yet ambiguous, group consciousness shaped by two prevalent discourses: social stratification among Muslims in India; and emerging activist platforms claiming to represent a Dalit Muslim polity. The ways in which ‘Dalit Muslims’ are reified as a presumably singular social group are highlighted (and disputed) in order to further scholarly debate regarding dynamics of group formation and definition. The analysis shows that, given similar social, economic and political experiences of some segments of the population, ‘Dalit Muslims’ may be treated (cautiously) as a social category for purposes of discussion. Nevertheless, despite enduring discourses about social hierarchy and socio-political activism, and a generalized have-nots versus elite rhetoric that underlies assertions of community coherence and demands for amelioration, no established, homogeneous group appropriate for either scholarly investigation or policy planning can be identified. Rather, diversity, status ambiguity and ongoing change processes provide the most cogent characterization of Dalit Muslim communities in India today.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.071 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| 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".