Broadening the audience for science engagement with machine-learning techniques

Abstract

Read online

In Switzerland, the need to engage the public in science is rooted in the political system with its tools of initiatives, therefore the broadening of the audience is critically important. Using the 2021 Science and Technology Eurobarometer, we propose solutions by using machine-learning techniques which identified patterns of engagement and the interaction of sociodemographic characteristics that constitute the prediction (1) of lowest level of science engagement and (2) of non-visitors to the science and technology museum. The techniques allow a more precise targeting than traditional segmenttion analyses.

Keywords

• public engagement with science and technology
• non-public
• machine learning
• S&T museum
• science communication