The epistemic attitudes of scientists, such as epistemic tolerance and authoritarianism, play important roles in the discourse about rivaling theories. Epistemic tolerance stands for the mental attitude of an epistemic agent, e.g., a scientist, who is open to opposing views, while epistemic authoritarianism represents the tendency to uncritically accept views of authorities. Another relevant epistemic factor when it comes to the epistemic decisions of scientists is the skepticism towards the scientific method. However, the question is whether these epistemic attitudes are (...) influenced by their sociopolitical counterparts, such as the researcher’s degree of conservatism. To empirically investigate the interplay between epistemic and sociopolitical attitudes of scientists, we conducted a survey with researchers (N = 655) across different disciplines. We propose scales for measuring epistemic tolerance and epistemic authoritarianism, as well as a scale for detecting the participants' readiness to question the scientific method. Furthermore, we investigate the relationship between epistemic tolerance and epistemic authoritarianism on the one hand, and career stage and sociopolitical views on the other hand. Interestingly, our study found only small correlations between the participants' degree of conservatism and their epistemic attitudes. This suggests that political views, against common argumentation, actually do not play an important role in one’s scientific decisions. Moreover, social scientists scored higher on the epistemic tolerance and lower on the epistemic authoritarianism scale than natural scientists. Finally, the results indicate that natural scientists question the scientific method less than social scientists. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with the (...) goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Private funding of life sciences has been extensively criticized as lacking objectivity (e.g., Bekelman et al. 2003). However, it is also important to point out that public funding of life sciences faces many objections. In order to improve the system of publicly funded life sciences and its ability to respond to global health challenges such as the COVID-19 pandemic, we should focus on several aspects. First of all, providing existential stability for researchers, in turn, could result in the decrease of (...) academic misconduct. Secondly, COVID-19 vaccines come from all parts of the world and have to be distributed throughout the world, which is a strong argument in favor of the positive stimulation of global science conducted at less-known research institutions and throughout all countries. This brings us to the quest of working on epistemic decolonization and inclusion in contrast to the current elitist paradigm in science. Finally, publicly funded research, which is at the moment mainly focused on foundational questions, should also be extended to applied ones. Public funding of applied research would lead to its development independently of private financial interests. (shrink)

The question of when to stop an unsuccessful experiment can be difficult to answer from an individual perspective. To help to guide these decisions, we turn to the social epistemology of science and investigate knowledge inquisition within a group. We focused on the expensive and lengthy experiments in high energy physics, which were suitable for citation-based analysis because of the relatively quick and reliable consensus about the importance of results in the field. In particular, we tested whether the time spent (...) on a scientific project correlates with the project output. Our results are based on data from the high energy physics laboratory Fermilab. They point out that there is an epistemic saturation point in experimenting, after which the likelihood of obtaining major results drops. With time the number of less significant publications does increase, but highly cited ones do not get published. Since many projects continue to run after the epistemic saturation point, it becomes clearer that decisions made about continuing them are not always rational. (shrink)