Jochen Gläser

Student participation in university research has typically been seen in terms of learning benefits for students; and the relevance of students in research processes has been largely neglected. This study addresses the gap in understanding student participation in research by comparing the link between epistemic properties of research processes and research fields on the timing and prevalence of mechanisms of participation in Modern German Literature and Experimental Condensed Matter Physics.

In their everyday work, scholars constantly acquire and transfer knowledge. Many of these knowledge flows are difficult to observe, not least because scholars are often not aware of them. This may be the reason why the attention to knowledge flows is very unevenly distributed across science studies, with bibliometric citation-based studies contributing the most research. Starting from the premise that bibliometric methods can be more readily exploited in the study of knowledge flows, this review explores the potential of bibliometric methods for the investigation of knowledge flows. Bibliometrics provides a portfolio of data and methods that can be used alone or in combination with qualitative methods to study knowledge flows. We organise contributions to the study of knowledge flows according to their object of study—formal, informal, or tacit knowledge—and according to the mode of flow—impersonal or interpersonal knowledge flow. The review shows that bibliometrics is strongly focused on the use of citation data for the investigation of impersonal flows of formal knowledge and has recently turned to the impersonal flow of informal knowledge via social media. In contrast, there are only few bibliometric studies that address interpersonal flows of knowledge. The review identifies an under-utilised potential of bibliometric methods and suggests some directions for future methodological development.

Global algorithms have taken precedence in bibliometrics as approaches to the reconstruction of topics from networks of publications. They partition a large set of publications, and the resulting disjoint clusters are then interpreted as individual topics. This is at odds with a sociological understanding of topics as formed by the participants working on and being influenced by them, an understanding that is best operationalized by algorithms prioritizing cohesion rather than separation, by using local information and by allowing topics to overlap. Thus, a different kind of algorithm is needed for topic reconstruction to be successful. Local algorithms represent a promising solution. In this paper, we present for consideration a new Multilayered, Adjustable, Local Bibliometric Algorithm (MALBA), which is in line with sociological definitions of topics and reconstructs dense regions in bibliometric networks locally. MALBA grows a subgraph from a publications seed by either interacting with a fixed network data set or querying an online database to obtain up-to-date linkage information. New candidates for addition are evaluated by assessing the links in two data models. Experiments with publications on the h-index and with ground truth data positioned in a data set of AMO physics illustrate the properties of MALBA and its potential.

Studies on research quality criteria and their application have largely not defined the concept of ‘research quality’. This reluctance to define and theoretically position the concept of research quality consigns empirical research to remain descriptive and makes methodological decisions more difficult. This paper aims to propose a theoretical approach to research quality by presenting a definition, grounding it in social theory, illustrating its applicability and exploring its methodological consequences for empirically investigating notions of research quality held by members of scientific communities.

In this paper I argue that the attempts by science studies to identify epistemic effects of new governance instruments have largely failed. I suggest two main reasons for this failure. The first reason is that neither quantitative nor qualitative studies of effects of governance instruments meet the respective methodological standards for establishing causality. While much of this could be repaired, the second reason is more severe: given the complex causal web between governance and knowledge production and the multi-level nature of causation, a strategy that starts from a particular governance instrument and tries to identify its effects cannot work. I propose to reverse this strategy by starting from the observation of epistemic change and applying a strategy of “causal reconstruction” (Mayntz), which identifies the causes of this epistemic change and among them the contribution by governance. This approach has the advantage of starting from well-identified change. Challenges posed by the new approach include the empirical identification of epistemic change and the need to integrate sociological methods in science policy studies.

The dominant approach to the reconstruction of scientific topics is the application of global community detection algorithms. Some of the properties of these algorithms, however, collide with the sociological discussion on topics. We present here for consideration a new local bibliometric algorithm that is in line with sociological definitions of topics and which reconstructs dense regions in bibliometric networks locally.