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Record W4405439541 · doi:10.20899/jpna.c7vvvv81

Philanthropy Under Uncertainty: Muslim Religious Giving During the COVID-19 Pandemic

2024· article· en· W4405439541 on OpenAlexaboutno aff
Osman Umarji, Rafeel Wasif, Shariq Siddiqui, Zeeshan Noor

Bibliographic record

VenueJournal of Public and Nonprofit Affairs · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyPolitical scienceMedicineInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

This study investigates the demographic, religious, and psychological correlates of charitable giving during times of uncertainty. We employ structural equation modeling to understand the mechanisms that underlie faith-based philanthropy amongst Muslims. For this purpose, we analyzed Muslim American philanthropy during the month of Ramadan in 2020, a month traditionally associated with increased religiosity and philanthropy. Utilizing a sample of 1,722 Muslims in the United States and Canada, uncertainty intolerance was associated with financial anxiety (B = .26), which in turn was related to donating less money (B = -.06). Financial anxiety was also associated with subjective financial well-being (B =.-.22), which was associated with donations (B = .11). We also found that income (B = .23), education (B = .30), and age (B = .28) positively predicted charitable giving. Increased religious practice (B = .07), such as prayer and reading scripture, was also associated with donating more money. Our results add valuable insights to the literature about the predictors and mediators of religious giving and philanthropy under uncertainty.

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)0.004
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 score0.059
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.051
GPT teacher head0.338
Teacher spread0.286 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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