A Taxonomy of Transparency in Science

Abstract

Both scientists and philosophers of science have recently emphasized the importance of promoting transparency in science. For scientists, transparency is a way to promote reproducibility, progress, and trust in research. For philosophers of science, transparency can help address the value-ladenness of scientific research in a responsible way. Nevertheless, the concept of transparency is a complex one. Scientists can be transparent about many different things, for many different reasons, on behalf of many different stakeholders. This paper proposes a taxonomy that clarifies the major dimensions along which approaches to transparency can vary. By doing so, it provides several insights that philosophers and other science studies scholars can pursue. In particular, it helps address common objections to pursuing transparency in science, it clarifies major forms of transparency, and it suggests avenues for further research on this topic.

Health Economists on Involving Patients in Modeling: Potential Benefits, Harms, and Variables of Interest

“…One possibility is to involve patients in HE modeling, which could help ensure models are acceptable to patients and incorporate their expertise [ 3 , 25 ]. Involving patients in HE modeling could also be a direct way to enhance transparency, while giving health economists and patients a chance to mutually inform social and ethical value judgments that arise in the HE modeling process [ 4 , 16 ]. From a theoretical perspective, the evidence that modeling requires making social and ethical value judgments [ 4 , 5 , 8 – 13 ] lends support to initiatives to involve patients in the process: at least, where scientists must make social and ethical decisions, the need for democratic accountability in science suggests consulting the people those decisions will affect [ 27 ].…”

“…However, such studies will require first addressing the overarching question: what benefits are sought by patient involvement in HE modeling? If one of the benefits is giving patients the opportunity to inform social and ethical value judgments in modeling, this raises several more key questions, many of which are philosophical [ 16 ]. Just some key questions include how best to describe value judgments in HE modeling, which value judgments patients should be invited to inform, and what other stakeholders—such as the general public—should given the same opportunity.…”

“…To manage value judgments in science, Elliott [ 6 ] (p.14) recommends following three principles: transparency (making value judgments clear), engagement (inviting people to scrutinize value judgments), and representativeness (informing value judgments with widely held social and ethical values). Although Elliott's recommendation invites further discussion [ 14 , 15 ], it provides theoretical support for involving patients in HE modeling; Elliott's [ 16 ] framework suggests patient involvement in HE modeling can enhance transparency and allow researchers and patients to inform value judgments together.…”

“…However, this literature has tended to focus on PPI in qualitative health research, and PPI in quantitative research may present unique variables, such as statisticians' ability to communicate about analytic decisions [ 19 ]. Furthermore, the health literature has not emphasized the role of PPI in managing value judgments in research, as literature in philosophy of science has done [ 4 , 16 ]. To guide future research, there is a need to identify potential benefits, harms, and variables relevant to patient involvement in HE modeling specifically, including those relevant to managing value judgments.…”

Open Science in regulatory environmental risk assessment

“…Fortunately, there are many opportunities to move Open Science forward, despite these challenges. Transparency in science can take many different forms, with varying goals, audiences, content, and avenues for disseminating information; thus, there are many ways to adjust Open Science initiatives to alleviate concerns (Elliott, 2020 ). For example, in response to the concern that preprints could contain faulty information that misleads decision makers, the prominent biology preprint server bioRxiv decided not to host preprints in most areas of clinical research; instead, a separate site, medRxiv, was developed with more stringent policies for screening papers (Kaiser, 2017 ).…”