How biological background assumptions influence scientific risk evaluation of stacked genetically modified plants

An analysis of research hypotheses and argumentations

Bibliographic Data

ID8248629
AuthorsElena Rocca (0000-0002-5222-0238, Norwegian University of Life Sciences, corresponding author), Fredrik Andersen (0000-0001-6519-9880, Norwegian University of Life Sciences)
Year2017
Volume13
Issue1
Pages11-11
Publication date2017-12-01
Open AccessYes
TypeARTICLE
VenueLife Sciences Society and Policy (JOURNAL)
Journal identifiersISSN: 2195-7819 • E-ISSN: 2195-7819
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1186/s40504-017-0057-7
PMID28804806
OpenAlexW2742321778
LanguageEN
Citations received4
References cited40

Scientific risk evaluations are constructed by specific evidence, value judgements and biological background assumptions. The latter are the framework-setting suppositions we apply in order to understand some new phenomenon. That background assumptions co-determine choice of methodology, data interpretation, and choice of relevant evidence is an uncontroversial claim in modern basic science. Furthermore, it is commonly accepted that, unless explicated, disagreements in background assumptions can lead to misunderstanding as well as miscommunication. Here, we extend the discussion on background assumptions from basic science to the debate over genetically modified (GM) plants risk assessment. In this realm, while the different political, social and economic values are often mentioned, the identity and role of background assumptions at play are rarely examined. We use an example from the debate over risk assessment of stacked genetically modified plants (GM stacks), obtained by applying conventional breeding techniques to GM plants. There are two main regulatory practices of GM stacks: (i) regulate as conventional hybrids and (ii) regulate as new GM plants. We analyzed eight papers representative of these positions and found that, in all cases, additional premises are needed to reach the stated conclusions. We suggest that these premises play the role of biological background assumptions and argue that the most effective way toward a unified framework for risk analysis and regulation of GM stacks is by explicating and examining the biological background assumptions of each position. Once explicated, it is possible to either evaluate which background assumptions best reflect contemporary biological knowledge, or to apply Douglas' 'inductive risk' argument

Biology · Economics · Epistemology · Philosophy of science · Political science · Positive economics · Realm · Sociology · Uncertainty · Computer Science · CRISPR and Genetic Engineering · Genetically Modified Organisms Research · Law · Mathematics · Philosophy · Transgenic Plants and Applications

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