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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".