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Record W4308652861 · doi:10.1177/14661381221134417

Autonomous care? Muslim transnational giving networks and perceptions of welfare responsibilities in India

2022· article· en· W4308652861 on OpenAlexaff
Catherine Larouche

Bibliographic record

VenueEthnography · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWelfareSociologyIslamState (computer science)EthnographyWelfare stateCitizenshipPolitical scienceEconomic growthPolitical economyEconomicsLawPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.281
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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