Autonomous care? Muslim transnational giving networks and perceptions of welfare responsibilities in India
Bibliographic record
Abstract
In Uttar Pradesh, many middle-class Muslims increasingly view local and transnational religious giving as a pragmatic way to create tangible socioeconomic improvements in the lives of underprivileged Muslims and mitigate their growing marginalization in India. How does their turn toward transnational religious giving influence their perception of the state’s responsibilities regarding social welfare provision? Based on ethnographic fieldwork research with registered non-profit organizations collecting and distributing Islamic alms ( zakat and sadaqa) in the state of Uttar Pradesh, this article examines how local and transnational religious giving affects the ways in which members of these Muslim philanthropic organizations imagine citizenship and welfare responsibilities in India. Distributive practices within these organizations show a dual focus on fostering Muslims’ economic independence and self-sufficiency by mobilizing local and transnational charitable networks on the one hand and improving access to state welfare on the other. The co-existence of these somewhat divergent strategies suggests that while the state is considered partial and uncaring, it also remains viewed as an indispensable welfare provider. More generally, these observations bring forth a discussion on the extent and effects of the transnationalisation and privatisation of welfare in globally connected South-Asia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".