Cooperation and Trust Across Societies During the COVID-19 Pandemic
Bibliographic record
Abstract
Cross-societal differences in cooperation and trust among strangers in the provision of public goods may be key to understanding how societies are managing the COVID-19 pandemic. We report a survey conducted across 41 societies between March and May 2020 ( N = 34,526), and test pre-registered hypotheses about how cross-societal differences in cooperation and trust relate to prosocial COVID-19 responses (e.g., social distancing), stringency of policies, and support for behavioral regulations (e.g., mandatory quarantine). We further tested whether cross-societal variation in institutions and ecologies theorized to impact cooperation were associated with prosocial COVID-19 responses, including institutional quality, religiosity, and historical prevalence of pathogens. We found substantial variation across societies in prosocial COVID-19 responses, stringency of policies, and support for behavioral regulations. However, we found no consistent evidence to support the idea that cross-societal variation in cooperation and trust among strangers is associated with these outcomes related to the COVID-19 pandemic. These results were replicated with another independent cross-cultural COVID-19 dataset ( N = 112,136), and in both snowball and representative samples. We discuss implications of our results, including challenging the assumption that managing the COVID-19 pandemic across societies is best modeled as a public goods dilemma.
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 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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".