Trust in scientifc information mediates associations between conservatism and coronavirus responses in the U.S., but few other nations
Bibliographic record
Abstract
U.S.-based research suggests conservatism is linked with less concern about contracting coronavirus and less preventative behaviors to avoid infection. Here, we investigate whether these tendencies are partly attributable to distrust in scientific information, and evaluate whether they generalize outside the U.S., using public data and recruited representative samples across three studies (Ntotal = 34,710). In Studies 1 and 2, we examine these relationships in the U.S., yielding converging evidence for a sequential indirect effect of conservatism on compliance through scientific (dis)trust and infection concern. In Study 3, we compare these relationships across 19 distinct countries. Although the relationships between trust in scientific information about the coronavirus, concern about coronavirus infection, and compliance are consistent cross-nationally, the relationships between conservatism and trust in scientific information are not. These relationships are strongest in North America. Consequently, the indirect effects observed in Studies 1–2 only replicate in North America (the U.S. and Canada) and in Indonesia. Study 3 also found parallel direct and indirect effects on support for lockdown restrictions. These associations suggest not only that relationships between conservatism and compliance are not universal, but localized to particular countries where conservatism is more strongly related to trust in scientific information about the coronavirus 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 | 0.004 | 0.031 |
| 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) | 0.006 | 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".