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We examined the role of peer review in quality assurance system developed for regulation higher education institutions in Russia. Given the negative attitude to the state attempts to increase control over universities, why academics volunteered to participate in regulative activity? This article applies the resource allocation theory to explain the engagement in academic service depending on people’s success in their primary job. Results indicate that Russian academics who performed lower in respect with publications and citations in the selective journals are more likely to become an expert engaged in academic citizenship in the form of inspections.

Mikhail Sokolov - Candidate of Sciences in Sociology, Professor. E-mail: msokolov@eu.spb.ruKaterina Guba - Candidate of Sciences in Sociology, Director of the Center for Institutional Analysisof Science & Education. E-mail: kguba@eu.spb.ruAngelika Tsivinskaya - junior researcher at the Center for Institutional Analysis of Science & Education. E-mail: atsivinskaya@eu.spb.ruEuropean University at St. Petersburg. Address: 6/1a Gagarinskaya Str., 191187 St. Petersburg, Russian Federation.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.

Recently, we are witnessing an aspiration in the social sciences to collect and analyze the data about human behavior that is being produced with an unprecedented depth and scale. In this article, we discuss how this new data may impact sociology. Big Data has been defined in various ways in literature. Some of the latest works reveal that the key definitional boundary marker is not the volume of data produced, but the traits of velocity and exhaustivity. The differences of this new type of data are that it is not created for research purposes, that it covers the entire population, and that it is produced in real-time. There are two ways to answer the question of key changes in sociology in the era of Big Data. First, new online-data can greatly improve traditional sociological subfields which were prevented from being developed because of a lack of data. Now, there are new results based on online-data which shed light on the causal effect of social influence. Big data can also enable the development of new lines of research because of rapidly-developing computational techniques. This is especially important for those research areas which deal with large bodies of text, and most importantly, new techniques can greatly improve the sociology of culture where empirical research has been less developed when compared with theoretical ideas. Secondly, new data can have an impact on the disciplinary project of sociology. The article ends with the discussion of how Big Data can be used to support data-driven sociology, which differs from mainstream sociology where hypotheses are offered a priori, data is collected, and analyses are conducted to determine the degree to which the hypotheses are supported.