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  1. The Value-Free Ideal of Science: A Useful Fiction? A Review of Non-epistemic Reasons for the Research Integrity Community.Jacopo Ambrosj, Kris Dierickx & Hugh Desmond - 2023 - Science and Engineering Ethics 29 (1):1-22.
    Even if the “value-free ideal of science” (VFI) were an unattainable goal, one could ask: can it be a useful fiction, one that is beneficial for the research community and society? This question is particularly crucial for scholars and institutions concerned with research integrity (RI), as one cannot offer normative guidance to researchers without making some assumptions about what ideal scientific research looks like. Despite the insofar little interaction between scholars studying RI and those working on values in science, the (...)
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  • Social values influence the adequacy conditions of scientific theories: beyond inductive risk.Ingo Brigandt - 2015 - Canadian Journal of Philosophy 45 (3):326-356.
    The ‘death of evidence’ issue in Canada raises the spectre of politicized science, and thus the question of what role social values may have in science and how this meshes with objectivity and evidence. I first criticize philosophical accounts that have to separate different steps of research to restrict the influence of social and other non-epistemic values. A prominent account that social values may play a role even in the context of theory acceptance is the argument from inductive risk. It (...)
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  • Who’s afraid of the big bad wolf? The interweaving of values and science.Helena Likwornik - 2015 - Canadian Journal of Philosophy 45 (3):382-403.
    The role of values in the scientific process is widely debated. But evidence and values cannot be neatly separated. Instead, values infuse the entire scientific process, starting with the choice of research questions. Research avenues are selected based on prior beliefs about the workings of the world. In fact, informally assigned prior probabilities and normalizing constants play an essential role in distinguishing causes from correlations and ignoring irrelevant associations that would otherwise be suggested by raw data. But since these initial (...)
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