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Publication-based metrics have caused a number of strategic responses regarding scientists’ publication practices, including involvement in questionable research practices. One of such practices is the fictitious co-authorship that allows authors easily to increase the number of publications. This study examines the changes in publishing behavior in the Russian social sciences from 2014 to 2017 by comparing co-authorship patterns in non-predatory and predatory journals. We examined the proportion of solo articles and co-authorship articles, the types of co-authorships (institutional and national), and the number of authors per article. The results show that in the social sciences the two sets of articles are significantly different. The papers in predatory journals are more often published in co-authorship, and if in the early years of publication pressure such articles were written by authors from the same university, then papers with authors from different universities and regions began to appear more often. Predatory papers also demonstrate the prevalence of the certain number of authors per article — in non-predatory journals, articles written by five or more authors appear very rarely, while in predatory journals such publications dominate. This transition signals that paper mills have started to dominate in predatory business serving as broker between authors willing to have indexed publications.

Annual Survey of Performance of Higher Education Institutions, conducted in Russia since 2012, was used to identify “inefficient” universities, subject to measures including closure or merging with other schools. As a result of these policies, the number of Russian universities has decreased more than 1.5 times since 2013. In this article, we analyze the consequences of implementing the appraisal system invented by the Russian Ministry. We argue that the use of the Survey reflects a conceptual confusion between effectiveness (the organization’s ability to achieve socially significant goals) and efficiency (the ability to achieve goals with minimal cost). The Ministry has made managerial decisions based on the Survey results (like merging a public university with an allegedly better performing HEI), which indicates understanding of the Survey as an assessment of efficiency. At the same time, statistical analysis of the performance indicators demonstrates that structural characteristics of universities (region and belonging to an institutionalized category, e. g. a pedagogical or an agrarian university) explain the significant part of the variance in the university’s performance. This led to discrimination against certain “unlucky” categories of universities. The methods used include logistic regression to estimate the odds of being labeled as an effective organization in 2014 and the Cox proportional hazards model to estimate the university’s chances of survival between 2013 and 2017.