Charity Behavior During COVID-19 Pandemic: Explaining the Peculiarity
Bibliographic record
Abstract
The COVID-19 pandemic has affected the economies throughout the globe including countries in ASEAN region. In Indonesia, economic growth is predicted to be negative and recession is happening, starting from the third quarter of 2020. Ironically, Islamic social activities including charity giving has shown an encouraging development during the COVID-19 period as collected charity funds has been the highest amidst the height of pandemic. While this phenomenon seems to be impossible, it is definitely worth a research. This study analyses charity behavior during COVID-19 pandemic and aims to elaborate its significant determinants. Although the economy is badly affected by pandemic situation, people are still eagerly giving charity as to implement Islamic value of brotherhood and helping each other, especially during this difficult period. Logistic regression is used as method to assess whether the society tend to give charity or not amidst the pandemic. Income, shopping habit during pandemic, investment habit during pandemic, religiosity and subjective norm are found to have significant effects on charity giving during the pandemic. Suitable and effective efforts to assist the poor during and post-COVID-19 period can be strategize based on the factors identified in this study. Government and practitioners are encouraged to keep on going in establishing programs to help societies living during pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".