Introduction
The Berlin Science Survey (BSS) is a trend study that surveys the experiences and assessments of scientists in the Berlin research area every two years. The study focuses on the development of research practices and research culture in Berlin as a centre of science, which could experience significant dynamics not least due to umbrella-organisations such as the Berlin University Alliance (BUA) or the Berlin Research 50 (BR 50). The aim of the project is to empirically accompany this change. The experiences and assessments of the scientists, as well as the sometimes very different field-specific cultural conditions, are to be incorporated into a critically reflective monitoring of intended and unintended effects of science policy control. Further information on the study can be found on the website:
https://www.berlinsciencesurvey.de/en
After the pilot study in the winter semester of 2021/22, the second wave was conducted at the beginning of 2024. Information on the sample and the type of survey can be found here:
https://www.berlinsciencesurvey.de/en/documentation/methods-reports
For the analyses in this report, responses from 2,767 scientists from the Berlin research area were evaluated, including 2,032 scientists from the four BUA institutions and a further 735 from non-university research institutions in Berlin. In addition, 2,471 scientists from universities of excellence outside Berlin were surveyed. They serve as a comparison sample to check whether individual results are only valid for the Berlin area or also beyond.
The current wave also makes it possible for the first time to draw conclusions about the changes that have taken place in the Berlin research area over the last two years. To this end, some of the questions from the first wave were included again.
The second wave focuses on the question of what good conditions for science and research are. In particular, it addresses aspects that can actually be influenced by science policy actors and university management. While scientists and scholars are subject of a wide range of assessment and evaluation processes in which they are often judged quite one-sidedly based on their output, the Berlin Science Survey gives them the opportunity to evaluate their research environment and working conditions. With the results of the Berlin Science Survey, we provide data and perspectives that complement the usual research information. The survey-based research information can provide insights into how political governance and organisational decision-making work and where they may fail to achieve their actual goals or lead to unintended and otherwise unobserved effects.
The survey covered several topics that link the structural conditions to the research cultures that develop under these conditions. These include the extent of competition at various levels, resources and support from the institutions, work culture(s) and motivation, as well as workloads. Research quality is examined in terms of both the orientations and practices of researchers and placed in the context of motivation and workloads. In addition, the study investigates which research cultures are conducive to research output and research quality and which are not.
Research Cultures
It is essential for all analyses to consider the various research and work cultures. “Science” is not a homogeneous entity. Much more, the different subject fields are associated with different disciplinary cultures which are associated with different research practices (Knorr Cetina and Reichmann 2015). The research institutions, in turn, create research conditions in which the individual disciplinary cultures develop differently, sometimes locally. Not all scientific fields react to set conditions and control measures in the same way. Due to disciplinary differences in research practices, some fields find it easier than others to fulfil emerging expectations or to come to terms with requirements or regulations, for example in the context of evaluation and regulation processes. Therefore, acceptance or resistance often has less to do with the individual attitudes of the people involved than with the adaptability of a research field with regard to certain control and evaluation instruments.
For ensuring the accuracy of fit of control instruments and measures, the consideration of disciplinary differences should be a goal of modern higher education policy management in order to avoid frictions, frustrations and other unintended effects.
Self-Selection
To underpin the importance of (shaping) structural conditions, it is helpful to look at the self-selection processes in science. Unlike selection processes (e.g. selection procedures), self-selection processes are often underestimated in the higher education policy debate, yet they are key to the goal of attracting and retaining the best minds. Like selection processes, self-selection processes take place at every stage of an academic career. Graduates decide whether they want to do a doctorate after their Master's degree or enter the workforce immediately. Doctoral graduates decide whether they want to remain in academia and possibly pursue an academic career. Postdocs decide whether they want to pursue a professorship, whether they want to pursue alternative paths to remain in academia, or whether they want to leave academia altogether. At the same time, a lot is happening in the labour market: Generation Z are questioning previous standards, and companies are responding to the changing demands of their employees. However, the opportunities here are very field-specific. For all these individual decision-making processes, the assessments and evaluations of the structural conditions are relevant. How attractive are the conditions? How attractive is the location? How attractive is a position in science in general and a professorship in particular? All of this is compared with other professions and jobs, including those outside science.
Research Quality
From the perspective of science studies, research quality is one of the most difficult topics to address. On the one hand, it is unclear what exactly is meant by it. On the other hand, possible dimensions of research quality are often difficult to measure and quantify (Peterson and Panovsky 2021). The flood of ever-new research goals and metrics provided for them has already led to significant criticism (Wilsdon et al. 2015). Qualitative assessments, especially through peer-review, are given preference when evaluating research performance (CoARA 2022). But why should researchers be given guidelines at all? And why is there such a strong focus on research output, especially when it comes to performance targets?
If we only look at research output, we overlook the conditions under which the output is actually produced – the research cultures that emerge. They set the course for output and also lay the foundation for quality. To put it somewhat exaggerated we could say that everything that could be of interest regarding the quality of the output is already inherent in the research cultures and research practices, which in turn are significantly influenced by the structural conditions. Is the entire research culture geared towards quality? Or is it only geared towards quantity? Is the workload so high that quality often has to be compromised, including in research tasks?
Looking at research cultures and practices helps to identify weak points and undesirable developments at an early stage and at the root. This opens up the possibility of adapting structural and organisational contextual conditions accordingly if research cultures develop unfavourably.
Such a change of perspective, away from output control (ex post) and towards the design of structural conditions for the development of research cultures, is also accompanied by greater trust in scientists. They have a great sense of quality and know best which priorities they have to set in order to implement good research in their respective fields. For this, there is initially no need for external incentive structures. On the contrary, there is a risk of overriding and misdirecting through any form of incentive control. It is therefore important to know the perspectives of scientists from different research contexts and to take them along in the sense of participatory governance in change processes. Only this way ensures that change actually leads to improvements and relief of burden instead of new burdens or other unintended effects.