Disagreements about how research data are interpreted can have serious consequences
There is always more than one way to interpret research data. Researchers are now proposing a new way of agreeing on the interpretation of data. A serious consequence if we do not agree on a common way of explaining research data is that new policies are formulated on the wrong basis.
There is always more than one way to interpret research data. Fotoillustration: Pixabay.com.
In a new paper series that so far includes three articles, reseachers argue that basic philosophical inquiry can improve evidence-based policy.
The latest paper “Underdetermination and evidence-based policy Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences” published in august 2020, argues that experts working in evidence-based policy should introduce investigations into ontological background assumptions as part of their toolbox.
An ontological background assumption is an assumption about what the basic entities of a particular field are. Such beliefs, although rarely debated, influence how a scientist thinks about methodology and evidence evaluation.
A constant challenge for evidence-based policy is that although different groups analyse the same data, they often explain those data differently. These differences motivate diverging policies. So how should one decide what the evidence really says?
Typically, we set up expert panels that predominantly consist of specialists in methodological analysis. However, these specialists must choose a hierarchy of methods. Ontological background assumptions shape this hierarchy.
In their most recent paper, Andersen and Rocca argue that expert panels should look for a unity between the ontological background assumptions of a specific case, and those of the broader relevant scientific field. The explanation that unifies better is to be preferred.
The argument addresses cases where there is evidential underdetermination, i.e., cases where there are multiple, defendable ways to understand the same evidence. Historical cases are supplemented by a more thorough analysis of the ongoing debate over how one should regulate the production of GMstacks. A GMstack is a conventionally bred plant whose parent plants are genetically modified.
There are central differences in how the genetically modified plants are tested and regulated across the world. Rocca & Andersen 2017 found that the arguments in this debate are motivated in part by scientist’s ontological commitments.
This echoes traditional philosophical concerns that unspoken and non-empirical commitments often prompt presumably empirically motivated positions. Andersen, Anjum, and Rocca (2019) explores this topic in more detail.
In the most recent paper in this series, Andersen and Rocca argue that expert groups might be more effective and robust by including a principle of ontological unity in evidence evaluation. The next natural step is to see how this strategy can be generalised.
Andersen, Fredrik & Rocca, Elena (2020). Underdetermination and evidence-based policy. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences. ISSN 1369-8486. . doi: https://doi.org/10.1016/j.shpsc.2020.101335
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