Framing COVID-19 Preprint Research as Uncertain: A Mixed-Method Study of Public Reactions
Bibliographic record
Abstract
During the COVID-19 pandemic, journalists were encouraged to convey uncertainty surrounding preliminary scientific evidence, including mentioning when research is unpublished or unverified by peer review. To understand how public audiences interpret this information, we conducted a mixed method study with U.S. adults. Participants read a news article about preprint COVID-19 vaccine research in early April 2021, just as the vaccine was becoming widely available to the U.S. public. We modified the article to test two ways of conveying uncertainty (hedging of scientific claims and mention of preprint status) in a 2 × 2 between-participants factorial design. To complement this, we collected open-ended data to assess participants' understanding of the concept of a scientific preprint. In all, participants who read hedged (vs. unhedged) versions of the article reported less favorable vaccine attitudes and intentions and found the scientists and news reporting less trustworthy. These effects were moderated by participants' epistemic beliefs and their preference for information about scientific uncertainty. However, there was no impact of describing the study as a preprint, and participants' qualitative responses indicated a limited understanding of the concept. We discuss implications of these findings for communicating initial scientific evidence to the public and we outline important next steps for research and theory-building.
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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 | 0.080 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".