Analysis of biased language in peer-reviewed scientific literature on genetically modified crops

Bibliographic Data

ID15545645
AuthorsBo Stevens (0000-0002-6249-3459, United States Department of Agriculture, corresponding author), Randi Reppen (0000-0001-5657-9195, Northern Arizona University), Mark Linhart (New Orleans Public Library), Kara Skye Gibson (0000-0003-2081-9315, Northern Arizona University), Kara Gibson, Adrah Parafiniuk (0009-0009-5298-9002, Northern Arizona University), Aradhana Roberts (0000-0003-1671-3702), Robert L Sanford (0000-0003-2391-9231, Northern Arizona University), Robert Sanford, Nancy Collins Johnson (0000-0001-7077-6232, Northern Arizona University)
Year2021
Volume16
Issue8
Pages084035-084035
Publication date2021-07-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac1467
OpenAlexW3178716086
LanguageEN
References cited41

Social, political, and economic forces may inadvertently influence the stance of scientific literature. Scientists strive for neutral language, but this may be challenging for controversial topics like genetically modified (GM) crops. We classified peer-reviewed journal articles and found that 40% had a positive or negative stance towards GM crops. Proportion of positive and negative stance varied with publication date, authors’ country of origin, funding source, and type of genetic modification. Articles with a negative stance were more common at the beginning of the millennium. Authors from China had the highest positive:negative ratio (8:1), followed by authors from the USA (12:5) and the EU (5:7). Positive stance articles were six times more likely to be funded by private sources compared to those with a neutral or negative stance. Articles about glyphosate were more likely to be negative compared to articles about Bacillus thuringiensis . Linguistic features of articles with positive and negative stances were used to train a random forest classifier that predicts stance significantly better than random chance. This suggests the possibility of an automated tool to screen manuscripts for unintended biased language prior to publication

Biology · Economics · Genetically modified organism · Political science · Politics · Positive economics · CRISPR and Genetic Engineering · Genetically Modified Organisms Research · Insect Resistance and Genetics · Psychology · Law

Tools

Open DOIOpen Access