- The Pandemic Pushed Publishing Into the Digital Realm. So What’s Next? This post is by Steve Sieck, president of SKS Advisors, a consulting firm serving publishers and information services providers. In it, Mr. Sieck argues that the pandemic hasn’t changed the future, it has merely accelerated it, and hastened the shift to “virtual modes of work, learning, and community.” He then discusses what this means for publishers ranging from university presses to the Big Five, concluding that all publishers should “recognize that a more virtual, digital future is not as far away as it used to be and plan accordingly.”
- Making decisions with confidence: the importance of editorial integrity In this Research Information post , Nandita Quaderi, editor-in-chief of the Web of Science at Clarivate, explains “what editorial integrity means for citation data and journal evaluation.” In particular, she discusses the work of the Web of Science Journal Citation Reports (JCR) in detecting “anomalous citation behaviour including where there is evidence of excessive journal self-citation or citation stacking…”
- Machine Learning + Libraries: A Report on the State of the FieldCurrent Cites According to Current Cites “This is an extensive report (almost 100-pp) looking into the possibilities and challenges of machine learning in libraries. The last section on recommendations is valuable in equipping libraries that are keen to implement machine learning applications and cultivating expertise in this area. There is even a short section on ‘Parallel Reports’ where the author acknowledges and discusses three other similar reports, which form a great literature review on this topic.”
- AI detects conflicts of interest in medical journals, spots unexpected trend [in AI in Healthcare] According to this post medical journals accepting reprint fees are much more likely to publish articles written by authors who received industry payments, according to a new analysis published in PLOS ONE.
The researchers tracked more than 128,000 articles from 159 different journals, developing a machine learning-based AI model capable of tracking individual conflicts of interest in English-language disclosure statements. Overall, the model found that articles written in journals accepting reprint fees were 2.81 times more likely to include potential conflicts of interest.