by Elena Rocca
One idea promoted by CauseHealth is that, when evaluating evidence, pre-existing theoretical frameworks count as much as the data. For instance, data from a certain trial assume a particular significance depending on the general background theoretical understanding we have when we interpret them. In this new CauseHealth article, Elena Rocca and Fredrik Andersen show that, when evaluating health risks related to the use of genetically modified plants in agriculture, different ontological starting points play an essential role for the final risk evaluation.
As a case study, the paper focuses on 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. The authors analyzed eight papers representative of these positions and found that, in all cases, additional premises are needed to reach the stated conclusions. The authors 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 evaluate which background assumptions best reflect contemporary biological knowledge. If a choice among assumptions is not possible based on the current scientific knowledge, it is still possible to make an informed and responsible choice of the best fitting assumption based on social considerations.
You can find the full text article here.
Causality, Complexity and Evidence in Health Sciences 