Related Experiment Video
Updated: Aug 8, 2025

Using Generative Art to Convey Past and Future Climate Transitions
Published on: March 31, 2023
Polarization of public trust in scientists between 1978 and 2018 Insights from a cross-decade comparison using
1University of Wisconsin-Madison, nli8@wisc.edu.
Abstract:
The U.S. public's trust in scientists reached a new high in 2019 despite the collision of science and politics witnessed by the country. This study examines the cross-decade shift in public trust in scientists by analyzing General Social Survey data (1978-2018) using interpretable machine learning algorithms. The results suggest a polarization of public trust as political ideology made an increasingly important contribution to predicting trust over time. Compared with previous decades, many conservatives started to lose trust in scientists completely between 2008 and 2018. Although the marginal importance of political ideology in contributing to trust was greater than that of party identification, it was secondary to that of education and race in 2018. We discuss the practical implications and lessons learned from using machine learning algorithms to examine public opinion trends.
More Related Videos
05:54Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy
Published on: September 8, 2023
06:42Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
Published on: September 28, 2018
Related Concept Videos
Stereotype Content Model
Regression Toward the Mean
Group Polarization
Bias in Epidemiological Studies
Hindsight Biases
Global Climate Change