Prosociality During COVID-19: Pathways Through Affect, Financial Stress, Well-being, and Collective Disempowerment across 39 Countries
Bibliographic record
Abstract
Overcoming the COVID-19 pandemic, which resulted in great loss of life worldwide and shook the global economy, required individuals' willingness and ability to behave prosocially. To contribute to the understanding of predictors of prosociality, we used multilevel models to test three previously established pathways to prosocial behavior, which we call the “broaden and build”, compensation, and incapacity pathways. We also tested whether these three paths are mediated by general well-being, and moderated by collective disempowerment, i.e., individuals’ belief that external societal forces have made it harder for people like them to function effectively. Participants from 39 countries (N = 59987) were surveyed on their willingness to engage in prosocial behaviors in the context of the pandemic. The “broaden and build” pathway was supported: positive affect was associated with willingness to engage in prosocial behavior via higher well-being. Two (in)capacity paths were also supported: financial strain and negative affect were both negatively associated with prosociality via lower well-being. A compensation pathway was also observed: Controlling for lower well-being, negative affect was associated with greater prosociality. Finally, differences in disempowerment moderated the affective pathways: higher disempowerment strengthened the positive association of positive affect with prosociality via well-being, and buffered the negative affect incapacity path.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".