Polarization of public trust in scientists between 1978 and 2018 Insights from a cross-decade comparison using interpretable machine learning.

Nan Li,Yachao Qian

doi:10.1017/pls.2021.18

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.

Highlights

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
An analysis of the Shapley values across the models suggests that political ideology made an increasingly important contribution to predicting trust between 1978 and 2018 when compared with demographic factors
Using interpretable machine learning (ML) algorithms as an alternative analytical method, this study delineated the polarization of public trust as a manifestation of the “increasing bimodality” process

Summary

Introduction

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. Despite the collision of science and politics witnessed by the country, the U.S public’s trust in scientists reached a new high in 2019, with 86% having “a great deal” or “fair amount” of confidence in scientists, surpassing trust in the military, elected officials, business leaders, and the news media (Funk et al, 2019; Funk et al 2020) Many believe that this surge in public trust is encouraging. As public trust in scientists appears to be polarized along political lines, it is imperative to precisely characterize the polarization trend and identify the group leading the change To achieve these goals, we conducted a secondary analysis of General Social Survey (GSS) data using interpretative machine learning (ML) algorithms. We compared the marginal contributions of political and demographic factors to predicting trust

Objectives

Methods

Results

Conclusion