Starting with “fake news” and continuing through a trying 2+-year pandemic, there has been an unprecedented politicization of science. Terms like misinformation, credible source, sheep and fear-mongering have become pervasive.
Now, the first study to examine why Americans download open-access, science-based papers has revealed a strong demand among everyday people for the highest quality information. It has also reinforced the necessity of open-access journals for all.
Researchers at the Georgia Tech School of Public Policy focused on downloads of National Academies of Sciences, Engineering, and Medicine (NASEM) consensus reports, widely considered among the highest credibility science-based literature. For their study, the team examined 1.6 million comments left on 6.6 million downloads of NASEM consensus reports since 2011—the first year the Academies offered the content for free. The comments were left in response to a prompt asking users how they planned to use the reports.
According to a machine learning (ML) algorithm applied to the comments, while 48% of the reports were downloaded for academic purposes, even more were accessed by people outside strictly educational settings. In fact, BERT—the ML algorithm—noted the use of the word “edification” 3,700 times in the data set, something the researchers say “indicates a strong desire for lifelong learning among users.”
The 52% of non-academic downloaders included ham radio operators, amateur astronomers, lifelong learning providers, and retirees interested in keeping up. About 150,000 downloads were categorized as having to do with “personal use,” including topics such as cannabis, dying, genetically engineered crops, evolution versus creationism, and reducing gun violence. More than 25,000 doctors and nurses downloaded reports with plans to use the details to improve their clinical work.
Interestingly, the analysis revealed thousands of veterans downloaded NASEM reports to use in their disability claims to the U.S. Veteran’s Administration—with NASEM’s 20 reports on Agent Orange and the health effects of burn pits and high noise levels among the most frequent downloads.
“This research will, hopefully, raise awareness about the positive returns that accrue to society from investments in institutions that democratize public access to high-quality research,” said co-author Ameet Doshi, a School of Public Policy Ph.D. student and the head of the Donald E. Stokes Library at Princeton University.
The use of BERT, the ML algorithm, comes from the lab of Omar Asensio, a co-author on the study and assistant professor at Georgia Tech. Asensio’s Data Science and Policy Lab has used deep learning techniques in recent years to advance knowledge in energy efficiency, sustainable plastics, and electric vehicle charging infrastructure. He says the use of BERT to classify behavioral evidence about public interest in scientific information further expands and advanced the use of machine learning in the social sciences.
“When you get data at this scale, especially when you have unstructured data that grows in real-time, there are practical limitations as to why this kind of behavioral information was not previously known,” said Asensio. “We are showing in a number of research areas that experimental approaches to curate human-labeled training data can boost the performance of popular supervised ML algorithms, at a level that can match or even exceed human performance.”
According to the study, the algorithm was able to identify the correct meaning of the comments about 84% of the time.
“This study shows strong demand among everyday Americans for the highest quality information to help improve the job they are doing, to help their relatives, neighbors, and communities, and in some cases simply to learn for learning’s sake,” said co-author Diana Hicks, a professor at the Georgia Tech School of Public Policy. “We never hear these stories because everyone is focusing on all the misinformation that goes out over social media.”