Analysis and news
‘Genuinely disruptive’ Michael Upshall discusses the impact of machine learning on scholarly communications
although larger publishers will be able to demonstrate the biggest cost savings and productivity benefits.
The last 30 or so years in scholarly communications have been marked by a steady trend towards digital content creation and delivery; particularly for journals, the industry has now moved from print to online. However, online delivery may mask underlying print- based attitudes, as is evidenced from the continuing preference academics have for reading PDF rather than any other format. In this regard, the advent of machine learning, because it involves a semantic engagement with the content, may in the end be more far-reaching, in fact genuinely disruptive, as it looks to challenge existing business processes. Here, for the first time, is a tool that can identify the meaning contained within articles. Machine learning will have an impact
throughout the publishing lifecycle, from discovery (the most obvious quick win) to authoring, classification and presentation. The publishers most likely to succeed from this new technology will be smaller, more innovative organisations that are less wedded to current orthodoxies of how academic publishing should take place,
History As with any industry, academic publishing has experienced waves of technical innovation during its existence: the switch from paper to online, the move to markup systems like SGML and then XML, and the rise of digital workflow systems. Most of these initiatives took place first with journals, with books following behind slowly, and usually in a more partial way. Most commonly, the driver of change has been the need to reduce costs. The explosion of journal publishing in the second half of the 20th century compelled publishers to look to more efficient solutions to manage the workload. Initially, outsourcing was widely adopted – using offshore companies in India or the Philippines. This delivered an immediate improvement in publishers’ margins. However, labour costs inevitably rise, and the need for further improvements in efficiency led to many experiments in implementing automated workflow. One major enhancement delivered via XML, for example, was the switch
to management by exception. Instead of manually checking every article, publishers found they could set up systems that only alerted them if there was a problem in the XML conversion, when something went wrong. Improvements in the workflow such as these dramatically improved the efficiency of the publishing process, but did not impact the quality of research. For many years, machine learning was simply another technical innovation with potential for change but little real impact. Like linked data, the technology was known but had not been widely adopted; as with linked data, it was thought to be complex and to require considerable IT involvement for large- scale implementation.
“Academic publishing has experienced
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waves of technical innovation during its existence”
16 Research Information June/July 2017
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