Self-uncertainty and conservatism during the COVID-19 pandemic predict perceived threat and engagement in risky social behaviors
Bibliographic record
Abstract
Two studies ( N = 676) highlight the nuanced relationship between conservatism and adherence to COVID-19 policy and recommendations intended to slow the spread of the pandemic in the United States. Study 1 provided evidence that conservative Americans who felt uncertain about themselves and the future experienced elevated levels of symbolic threat (attacks to sociopolitical identity; e.g., the pandemic threatening American democracy) and realistic threat (concrete attacks to material resources or well-being; e.g., the pandemic threatening physical health) in comparison to their more certain counterparts. In Study 2, the association between this form of uncertainty and frequency of risky social behaviors (behaviors that increase the risk of virus transmission) was partially mediated by threat perception for Americans both low and high in conservatism. We discuss findings as an integration of the motivated social cognition framework and uncertainty-identity theory. While self-uncertainty was more associated with greater overall COVID-19 threat perception for Americans high (vs. low) in conservatism, threat perception and frequency of risky social behaviors were associated with self-uncertainty in a manner that is consistent with prevailing liberal and conservative norms.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".