Unreviewed science in the news: The evolution of preprint media coverage from 2014-2021
Bibliographic record
Abstract
Abstract It has been argued that preprint coverage during the COVID-19 pandemic constituted a paradigm shift in journalism norms and practices. This study examines whether, in what ways, and to what extent this is the case using a sample of 11,538 preprints posted on four preprint servers—bioRxiv, medRxiv, arXiv, and SSRN—that received coverage in 94 English-language media outlets between 2014–2021. We compared mentions of these preprints with mentions of a comparison sample of 397,446 peer reviewed research articles indexed in the Web of Science to identify changes in the share of media coverage that mentioned preprints before and during the pandemic. We found that preprint media coverage increased at a slow but steady rate pre-pandemic, then spiked dramatically. This increase applied only to COVID-19-related preprints, with minimal or no change in coverage of preprints on other topics. In addition, the rise in preprint coverage was most pronounced among health and medicine-focused media outlets, which barely covered preprints before the pandemic but mentioned more COVID-19 preprints than outlets focused on any other topic. These results suggest that the growth in coverage of preprints seen during the pandemic period may imply a shift in journalistic norms, including a changing outlook on reporting preliminary, unvetted research.
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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 | 0.009 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".