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FUTON (an acronym for full text on net) bias is a tendency of scholars in medical fields to cite academic journals with open access—that is, journals that make their full text available on the Internet without charge—in their own writing as compared with toll access publications. Scholars in some fields can more easily discover and access articles that have their full text on the Internet, which increases authors' likelihood of reading, quoting, and citing these articles.[1][2][3][4] Conversely, articles in expensive journals that do not provide open access (OA) are "priced out of evidence", giving FUTON publications greater utility.[5] FUTON bias may increase the impact factor of open access journals relative to journals without open access.[6]
One study concluded that authors in medical fields "concentrate on research published in journals that are available as full text on the internet, and ignore relevant studies that are not available in full text, thus introducing an element of bias into their search result".[6] Authors of another study conclude "that the OA advantage is a quality advantage, rather than a quality bias", that authors make a "self-selection toward using and citing the more citable articles—once OA self-archiving has made them accessible", and that open access "itself will not make an unusable (hence uncitable) paper more used and cited".[7]
No abstract available bias (NAA bias)—scholars' tendency to cite journal articles that have an abstract available online more readily than articles that do not—affects articles' citation count similarly to FUTON bias.[1][6]
Internet, Peer review, World Wide Web, Roarmap, Open source
World Wide Web, File sharing, Instant messaging, Email, IPv6
National Institutes of Health, Anonymity, Anonymous peer review, Academia, Nature (journal)
Peer review, Social sciences, Academic publishing, Internet, Economics
Research, Academic publishing, Bias, Academic, Abstract (summary)
Peer review, Law, Academic journal, Humanities, Computer science
Bias, Meta-analyses, Research, Peer review, Meta-analysis