Philanthropy Under Uncertainty: Muslim Religious Giving During the COVID-19 Pandemic
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".