Publications

This work proposes using the feature of large language models (LLMs) to learn about the "data bias" injected by diverse SDG classifications into bibliometric data by exploring five SDGs. We build a LLM that is fine-tuned in parallel by the diverse SDG classifications inscribed into the databases' SDG classifications. Our results show high sensitivity in model architecture, classified publications, fine-tuning process and natural language generation. The wide arbitrariness at different levels raises concerns about using LLM in research practice.

In the Dutch academic system, PhD theses can be awarded with cum laude and at most 5% of all PhD graduates receive this selective distinction for their thesis. In this paper, we investigate whether there is gender bias in awarding cum laude, using data from one of the major Dutch research universities. A main result is that the set of PhD theses receiving cum laude on average do not have a higher quality than the best theses not getting cum laude. A second main result is that, after controlling for the quality of the PhD theses, women still have a substantially lower probability to receive cum laude. These results strongly suggest that the distribution of awards suffers from gender bias.

Research funding systems are not isolated systems - but embedded in a larger scientific system with an enormous influence on the system. This paper aims to analyze the allocation of competitive research funding from different perspectives: How reliable are decision processes for funding? What are the economic costs of competitive funding? How does competition for funds affect doing risky research? How do competitive funding environments affect scientists themselves, and which ethical issues must be considered? We attempt to identify gaps in our knowledge and propose recommendations for policymakers and funding agencies, including empirical experiments of decision processes and collection of data on these processes.

The Scientists Survey was initiated in 2010. It is intended as a long-term barometer for science. Because the 2023 edition was the fourth wave, many topics, such as attitudes and opinions on science policy for example, can be examined over time in trends analyses. Additionally, the timing of the surveys waves has enabled a particular set of research questions: while the current cohort was surveyed directly at the end of the pandemic, the preceding wave took place directly before the start of the first extensive restrictions due to the COVID-19 pandemic. This "historical coincidence" gives insights into the way researchers worked in the time before, during and after the pandemic.