Lately, I have been thinking about how to start a research project on 'Who joins the debate?' (for lack of a better name). Who, when and why do we decide to start, join, or avoid a contested discussion? For example, within our own university walls, when and why do academics decide to (not) express their professional and personal opinions regarding DEI policies, genAI, the budget cuts, or the genocidal violence going on in Gaza? One hypothesis I would like to test is that people may not join political debates because they perceive the debate to be more polarized than it is in reality. I also would like to understand how and when coordination (e.g. "Let us both speak up together against ... .") may lead to more or less representative debates.

I try to hammer to point home that imho the best questions - that combine scientific innovation with societal relevance - arise out of a combination of theoretical knowledge, methodological skills and hands-on experience with different type of data (collection). And yes I know that this seems to contradict the hypothetico-deductive approach but it doesn't. While many of the students I teach want to 'save the world' I am struggling with the fact to so many of them are quite privileged and have not experienced themselves (or not even seem to know persons who have experienced) discrimination, poverty, etc. Don't get me wrong, I am definitely not saying that lived experience is essential for insight into social phenomena like inequality and segregation but it sure as hell does help in understanding what we are talking about in our - at times - abstract and somewhat detached scientific papers.

While the current budget cuts present many challenges, they also bring new opportunities. There is momentum for change, for example, to foster greater interdisciplinary collaboration within and between faculties and to regain focus on our core tasks: providing high quality education and research. If it is up to me: less bureaucracy (duh); less (graded) exams (and more time for teaching); less unscalable teaching innovation experiments (more adoption of effective innovative teaching methods and more teacher autonomy); less coaching by teachers (more attention for diverse student needs in University Teaching Qualification tracks).