Accountability in Research-Policies and Quality Assurance最新文献

Background: Polarized research has become a problem for the trustworthiness and applicability of scientific results. Accordingly, this paper addresses three key questions: 1) What is polarization in scientific research? 2) Why is such polarization problematic? 3) How can the problem be addressed?Methods: The first question is addressed by describing how the polarization has been characterized in the literature and by analysing an example before assessing existing definitions and elaborating a definition of polarization. The second question is answered by describing challenges with polarization found in the literature. The third question is addressed by investigating different explanations for and relevant mechanisms behind polarization in research, such as psychological, structural, epistemic and ontological, evaluative, and social-constructionist explanations. Moreover, several approaches from the philosophy of science are investigated.Results: Polarization in research is characterized by opposing and incommensurable positions that tend to stem from differences in basic values, and that are used to define, differentiate, bolster, and demarcate between groups and for reinforcing their identity. The problem with polarization is that it violates a broad range of basic norms in science, and hampers scientific progress, represents large opportunity costs, undermines trust in science and, subsequently that it undercuts the application of scientific results as well as future funding. There are many potential measures to reduce polarization. However, there are no simple solutions, as polarization is a complex phenomenon deeply rooted in basic human characteristics.Conclusion: Polarization is a ubiquitous phenomenon and a basic challenge for scientific research. It is crucial to increase the awareness of polarization, and a clear definition is key to study and address the problem. However, while there are many ways to actively address the problem of polarization in scientific research, there are no easy solutions. More research is needed to move from what we can do to what we should do.

Background: Since the advent of online research metrics, which began with Web of Science in 1997, these metrics have been increasingly used to rank researchers and universities. Over the last two decades, the easy access to research metrics has greatly benefitted the academic community and beyond by providing quantitative measures for ranking researchers, universities and departments. However, this accessibility, accompanied by a tendency to quantitatively evaluate research quality and impact, has also shifted the focus toward practices aimed at enhancing research metrics rather than pursuing high-quality, potentially path-breaking research. This trend threatens to degrade global research advancement and invalidate rankings.

Methodology: We perform an analysis of statistics from the Stanford's top 2% list and Nobel Laureates.

Results: We demonstrate that an accelerating number of researchers - on the order of 10% or 20,000 researchers on Stanford's Top 2% researchers - are achieving implausibly high-publication and new coauthor rates, with many producing tens to hundreds of papers per year, and gaining hundreds to thousands of new coauthors annually.

Conclusions: We propose a method to renormalize research metrics. Our renormalized metrics aim to remove the incentive for researchers to prioritize quantity or resort to unethical practices to boost their metrics.

Background: Codes of conduct for research integrity provide ambivalent guidance on the role that the values of society as well as political and economic interests can or should play in scientific research. The development of clearer guidance on this matter in the future should consider the attitudes of researchers.Methods: We conducted 24 semi-structured interviews with holders of grants from the European Research Council and performed an inductive thematic analysis thereof.Results: We developed 4 themes reflecting 4 main attitudes of researchers toward the interactions between values and science: awareness, concern, confidence, and embracement. While interviewees recognized that science is not completely value-free (awareness), they still seemed to hold on to the so-called value-free ideal of science as a professional norm to minimize bias (concern, confidence). However, they showed awareness of the beneficial influence that values like diversity can have on research (embracement).Conclusions: Codes such as the European Code of Conduct for Research Integrity tend not to problematize the tensions that emerge from having the value-free ideal of science as a norm and being guided by the values of society. Our findings suggest the time might be ripe for research integrity codes to address more directly the value issues intrinsic to science.

Background: In this paper, we explore the question "Why can't AI be a coauthor?" and reveal a rarely discussed reason.

Methods and results: First, allowing AI to be a coauthor disregards the uniquely human experience of writing texts. This means that human authors are seen as mere producers of texts rather than rational beings engaged in a value-added and humanized learning process expressed through the paper. The relationship between the human author and the thesis is reduced to a mere result of generation rather than a result of individual human critical thinking. Second, allowing AI to be a coauthor leads to self-delusion about one's own rationality and thus violates the responsibility to understand the world correctly. In this process of self-deception, it is not as if those who grant AI coauthor status do not realize that AI is not the same as humans; however, they self-deceivingly assume that AI has the same internal state as humans. This means that the relationship between the author and the work is no longer seen as a position to be respected, but as something probabilistic and gamified.

Conclusions: Finally, we discuss the potential consequences of these rationales, concluding that including AI as a coauthor implies a disregard for humanization.

Background: Universities are increasingly offering training in research integrity (RI) to enhance research quality and foster RI. Despite the importance of integrating scientific virtues into such training, there is a lack of assessment of virtue ethics-based RI training.

Methods: This was a randomised controlled study assessing the impact of a virtue-based training for RI, performed at the University of Split School of Medicine in 2020-2021. We included first-year medical students who were randomly assigned to a control group, receiving a RI lecture, or the interventional group, receiving the same lecture plus a virtue-based training for RI. We measured changes in Ethical Climate Questionnaire (ECQ) scores as the primary outcome and the difference between perceived and desired ECQ scores as the secondary outcome.

Results: Of 181 participants, 105 (55 control, 50 experimental) completed the study. The virtue-based training did not significantly change ethical climate perceptions between groups. Dominant climates were Company rules and procedures and Laws and professional codes. Overall, the preferred climates emphasized Team interest and Social responsibility.

Conclusions: This study demonstrates the value of using ECQ to assess RI training and highlights the need for further research into the long-term effects of virtue-based training.

Research is conducted in workplaces that can present safety hazards. Where researchers work in laboratories, safety hazards can arise through the need to operate complex equipment that can become unsafe if faulty or broken. The research literature also represents a workplace for millions of scientists and scholars, where publications can be considered as key research equipment. This article compares our current capacity to flag and repair faulty equipment in research laboratories versus the literature. Whereas laboratory researchers can place written notices on faulty and broken equipment to flag problems and the need for repairs, researchers have limited capacity to flag faulty research publications to other users. We argue that our current inability to flag erroneous publications quickly and at scale, combined with the lack of real-world incentives for journals and publishers to direct adequate resources toward post-publication corrections, results in the research literature representing an increasingly unsafe workplace. We describe possible solutions, such as the capacity to transfer signed PubPeer notices describing verifiable errors to relevant publications, and the reactivation of PubMed Commons.

Objective: This study aimed to compare the accuracy of metadata for retracted articles in Retraction Watch Database (RWD), PubMed, and Web of Science (WoS).

Methods: Twenty general internal medicine journals with an impact factor > 2 were randomly selected. RWD, PubMed, and WoS were used to retrieve all retracted articles published in these journals. Eight metadata variables were examined: journal, title, type of article, author(s), country/countries of affiliation, year of publication, year of retraction, and reason(s) for retraction (assessed only for RWD, as this information was unavailable in PubMed and WoS). Descriptive analyses were conducted to document errors across databases.

Results: Thirty-five retractions were identified, and 280 metadata entries (35 × 8) were analyzed. RWD contained the most metadata errors, affecting 16 articles and 20 metadata entries, including seven errors in year of publication, six in article type, six in author names (five misspellings, one missing two authors), and one in country of affiliation. WoS had one error (a missing author), and PubMed had none.

Conclusion: The relatively high error rate in RWD suggests that researchers should cross-check metadata across multiple databases. Given the preliminary nature of this study, larger-scale research is needed to confirm these findings and improve metadata reliability in retraction databases.