Bibliographic record
Abstract
Growing evidence suggests that outbreaks such as the COVID-19 pandemic are better handled in places where social capital is high. Less clear, however, are the channels through which social capital makes communities better able to respond to outbreaks. In this article I develop a multidimensional and multilevel approach that compares the potential dissimilar effects of social capital in different forms and at different levels. As social capital in different forms and at different levels can affect social outcomes through distinctive means, such an approach can help detect the processes underlying how social capital works. I illustrate this new approach by analyzing data from a survey I conducted in late April 2020 in China’s Hubei province as well as data from the most recent World Values Survey (WVS, 2016–2020). Results suggest that social capital affects COVID-19 response mainly through facilitating collective actions and promoting public acceptance of and compliance with control measures in the form of trust and norms at the individual level. Social capital can also help mobilize resources in the form of networks at the community level. In an authoritarian context, compliance with control measures relies more on people’s trust in their political institutions, less on trust in each other.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".