Ambiguous COVID-19 Messaging Increases Unsafe Socializing Intentions
Bibliographic record
Abstract
Before and during the vaccine roll out, governments reported surging COVID-19 cases due to unsafe socializing among younger individuals. Officials continue to search for effective ways to encourage safe socializing behaviour within this demographic. However, a key challenge is that public health advice is necessarily nuanced and complex, which can create ambiguity. Appropriate behaviour depends on specific circumstances and public messaging cannot detail every situation. When people confront ambiguity in expert guidance, they may engage in motivated reasoning—that is, people’s underlying motivations may influence how they process information and make decisions. In a pre-registered experiment, we look at the effect of ambiguous public health messaging on people’s inferences regarding the behaviours the government expects them to avoid and intentions to engage in unsafe socializing. We find no evidence of an effect on inferences—that is, people who receive an ambiguous message about COVID-19 make inferences about correct behaviour that are similar to the inferences of those who receive no message. However, we find ambiguous messaging increases unsafe socializing intentions, especially among people aged 18-39 who socialized before the pandemic. Our findings underscore the need for unambiguous communications during public health crises.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".