Forecasting the publication and citation outcomes of COVID-19 preprints

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dc.citation.issue9
dc.citation.volume9
dc.contributor.authorGordon M
dc.contributor.authorBishop M
dc.contributor.authorChen Y
dc.contributor.authorDreber A
dc.contributor.authorGoldfedder B
dc.contributor.authorHolzmeister F
dc.contributor.authorJohannesson M
dc.contributor.authorLiu Y
dc.contributor.authorTran L
dc.contributor.authorTwardy C
dc.contributor.authorWang J
dc.contributor.authorPfeiffer T
dc.coverage.spatialEngland
dc.date.accessioned2023-11-30T00:50:35Z
dc.date.accessioned2024-07-25T06:50:38Z
dc.date.available2022-09-28
dc.date.available2023-11-30T00:50:35Z
dc.date.available2024-07-25T06:50:38Z
dc.date.issued2022-09
dc.description.abstractMany publications on COVID-19 were released on preprint servers such as medRxiv and bioRxiv. It is unknown how reliable these preprints are, and which ones will eventually be published in scientific journals. In this study, we use crowdsourced human forecasts to predict publication outcomes and future citation counts for a sample of 400 preprints with high Altmetric score. Most of these preprints were published within 1 year of upload on a preprint server (70%), with a considerable fraction (45%) appearing in a high-impact journal with a journal impact factor of at least 10. On average, the preprints received 162 citations within the first year. We found that forecasters can predict if preprints will be published after 1 year and if the publishing journal has high impact. Forecasts are also informative with respect to Google Scholar citations within 1 year of upload on a preprint server. For both types of assessment, we found statistically significant positive correlations between forecasts and observed outcomes. While the forecasts can help to provide a preliminary assessment of preprints at a faster pace than traditional peer-review, it remains to be investigated if such an assessment is suited to identify methodological problems in preprints.
dc.format.pagination220440-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/36177198
dc.identifier.citationGordon M, Bishop M, Chen Y, Dreber A, Goldfedder B, Holzmeister F, Johannesson M, Liu Y, Tran L, Twardy C, Wang J, Pfeiffer T. (2022). Forecasting the publication and citation outcomes of COVID-19 preprints.. R Soc Open Sci. 9. 9. (pp. 220440-).
dc.identifier.doi10.1098/rsos.220440
dc.identifier.eissn2054-5703
dc.identifier.elements-typejournal-article
dc.identifier.issn2054-5703
dc.identifier.piirsos220440
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70995
dc.languageeng
dc.publisherThe Royal Society
dc.relation.isPartOfR Soc Open Sci
dc.rights(c) The Author/s 2022
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectforecasting
dc.subjectpreprinting
dc.subjectscience policy
dc.titleForecasting the publication and citation outcomes of COVID-19 preprints
dc.typeJournal article
pubs.elements-id457008
pubs.organisational-groupOther
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