  Large bibliometric databases, such as Web of Science, Scopus, and OpenAlex,
facilitate bibliometric analyses, but are performative, affecting the
visibility of scientific outputs and the impact measurement of participating
entities. Recently, these databases have taken up the UN's Sustainable
Development Goals (SDGs) in their respective classifications, which have been
criticised for their diverging nature. 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.
