DDRIG: Language of scientific uncertainty and risk in food safety and environmental science

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This project looks at government reports of food safety and environmental risk to compare discussions of risk and uncertainty. The reports studied will cover potential hazards to the public health. Using two decades of scientific policy reports the project will analyze different expressions of uncertainty within science communications. The analysis of regulatory documents will help protect the public when speaking of risks and uncertainty. The project will be of interest to scientists, policy makers, and the public. This project will assemble a dataset of sentences containing expressions of uncertainty within scientific reports published by regulatory agencies. It will do a comparative analysis of uncertainty expression within hazard-specific risk assessments commissioned by federal agencies. The researchers will develop a machine learning natural language processing (NLP) model to assist the human-supervised coding of documents published in the last two decades. The coding will focus on expressions by a taxonomy of specific forms of uncertainty. This project will yield longitudinal cases for comparison across two policy contexts. The quantitative comparative analysis component of the project will illustrate the interaction of knowledge formation and uncertainty expression within the epistemic cultures of governmental food safety science. The project will use the NLP system’s capabilities to detect uncertainties expressed around hazards in environmental risk assessment. The project’s findings will inform discussions of the suitability and risks of using emerging machine learning technologies for content analysis in the social sciences. This project will produce a human-verified dataset of scientists’ uncertainties around hazards that bear on the public’s health. By using governmental risk assessments which are openly published, the resulting database of uncertainty expressions can also be published online and made available to the public at large. This will allow interested or concerned citizen to examine how experts’ questions around the various risks to their health, stemming from their food or their environment, have changed over time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.