Publication and data surveillance in academia | Research Information

"It is becoming increasingly clear that the core functions of higher education are destined to be quantified and that this data will be harvested, curated, and repackaged through a variety of enterprise management platforms. All aspects of the academic lifecycle, such as research production, publication, distribution, impact determination, citation analysis, grant award trends, graduate student research topic, and more can be sold, analysed, and gamed to an unhealthy degree. By unhealthy, we mean constricted and self-consuming as the output we develop is directly contingent on the input we receive. Well-meaning tools, such as algorithmically derived research suggestions and citation analysis, create a shrinking and inequitable academic landscape that favours invisibly defined metrics of impact that are reinforced through further citation thereby limiting the scope and scale of research available.... As the shift to open access gains momentum, there is danger of the unintended consequences as enterprise platforms seek to maximise profit as the models shift from under their feet. As Alexander Grossmann and Björn Brembs discuss, the cost creep incurred by libraries reflects this pivot shifting to a model of author costs, which are often supported by libraries, thereby adjusting costing methods from the backend subscription model to the front-end pay to publish model. It is not surprising or controversial that for-profit enterprise, database, and academic platform vendors are seeking to turn a profit. We should remain vigilant, however, to academia's willingness to find the easy and convenient solution without considering the longer-term effects of what they are selling. In a recent industry platform webinar, academic enterprise representatives discussed the “alchemy” of user-derived data and their ability to repackage and sell this data, with consent, to development companies with their key take away being a driver towards increased revenue. More to the point, they had learned the lessons of the tech industry, and more specifically the social media companies in understanding the data we generate can be used to target us, to sell to us, to use us for further development. They discussed the ways in which the use of this data would become, like social media, intelligent and drive user behaviour – further cinching the knot on the closed-loop as algorithmically-based suggestions further constrain research and reinforce a status-quo enabled by profit motive in the guise of engagement, use, and reuse...."