Framing COVID-19 Preprint Research as Uncertain: A Mixed-Method Study of Public Reactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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encouraged to convey uncertainty surrounding preliminary scientific evidence, including mentioning when research is unpublished or unverified by peer review.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.122 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it