Open AccessTracing Long-term Value Change in (Energy) Technologies: Opportunities of Probabilistic Topic Models Using Large Data Sets
Author(s) -
Tristan de Wildt,
I.R. van de Poel,
E.J.L. Chappin
Publication year - 2021
Publication title -
science, technology and human values/science, technology, and human values
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.094
H-Index - 73
eISSN - 1552-8251
pISSN - 0162-2439
DOI - 10.1177/01622439211054439
Subject(s) - value (mathematics) , trace (psycholinguistics) , data science , computer science , tracing , probabilistic logic , sustainability , newspaper , management science , artificial intelligence , sociology , machine learning , engineering , ecology , philosophy , linguistics , media studies , operating system , biology
We propose a new approach for tracing value change. Value change may lead to a mismatch between current value priorities in society and the values for which technologies were designed in the past, such as energy technologies based on fossil fuels, which were developed when sustainability was not considered a very important value. Better anticipating value change is essential to avoid a lack of social acceptance and moral acceptability of technologies. While value change can be studied historically and qualitatively, we propose a more quantitative approach that uses large text corpora. It uses probabilistic topic models, which allow us to trace (new) values that are (still) latent. We demonstrate the approach for five types of value change in technology. Our approach is useful for testing hypotheses about value change, such as verifying whether value change has occurred and identifying patterns of value change. The approach can be used to trace value change for various technologies and text corpora, including scientific articles, newspaper articles, and policy documents.