ABSTRACT
The article analyzes the reasons for the important effect of the COVID-19 pandemic, which has become the catalyst for long-overdue decline in the authority of expert knowledge. The author claims that widespread access to information and scientific data results in the collapse of universal and monopolistic expert-scientific hierarchies of knowledge of a large society, controlled by the state. Scientific experts, who acted as the historical heirs of priests and shamans, have lost their privileged access to sacred knowledge, made public by the media and the Internet. This resulted in severe damage to the key function of expertise - legitimization of the political order and power elites. Experts without the status of agents of the state have become indistinguishable from ordinary citizens. The example of discussions between Waxers and Anti-Waxers shows that both sides are able to put forward convincing scientific arguments that rhetorically do not allow the authorities to bring the discussion about the effectiveness of vaccinations down to a completely unobvious dispute between enlightened state experts and uneducated obscurantists. It is in the most developed Western states where one can see a strong civil dissident movement that distrusts or calls into question the disciplinary regimes of collective coexistence, legitimized by the paternalistic rhetoric of concern from political elites. Accordingly, the elites in the background situation of strengthening the practices of heterarchy, post-truth and postmodernism can no longer rely on the usual metanarratives of the Enlightenment, which allowed them to monopolize the discourse of science in the name of progress and unconditional good, building hierarchies of knowledge-power convenient for their priorities. Since science, knowledge and information have long became public domain, the line between elites, experts and citizens in the field of access to science has become almost indistinguishable. The actual political problem is that the situation of collision of different paradigms, opinions and data is exactly the normal state of science, which is now transferred to the field of public discussions following the final secularization of science. Thus, the institution of expert knowledge turns into an unnecessary link in a situation of equal access of all interested parties to scientific data;to an institution that hardly would efficiently perform the functions of scientific legitimation of socially significant decisions in the foreseeable future.
ABSTRACT
Many scholars have been challenged by multi-attribute group decision-making problems that have stimulated the appearance of increasingly general models. Pythagorean fuzzy sets were a reaction by Yager who in 2013, suggested this model to improve the performance of intuitionistic fuzzy sets. Another hybrid model –soft expert sets– deals with uncertain parameterized information. It considers opinions of different experts, improving the single-agent experience of soft sets. N-soft expert sets and their fuzzy version, namely, fuzzy N-soft expert sets, consider the ratings given to objects by more than one expert with respect to relevant parameters. The arguments supporting the need for independent allocation of membership and non-membership degrees apply to the fuzzy expressions imposed on top of the benefits of the N-soft expert environment. These challenges converge on the formulation of a new hybrid model called Pythagorean fuzzy N-soft expert sets that improves upon Pythagorean fuzzy sets with the benefits of N-soft expert sets. We study their scope of application with practical examples. Afterwards we discuss certain basic operators (subsethood, complement, union and intersection), prove some of their remarkable properties, and provide the concepts of equal, agree, and disagree-Pythagorean fuzzy N-soft expert sets. We present an algorithm for group decision-making problems in this framework and we explore three applications of this methodology, namely, to the analysis of wheat varieties, employee selection, and recovery order of patients suffering COVID-19. In the end, we provide a sensitivity analysis comparing the proposed model with some existing models to guarantee its cogency and feasibility. © 2023 The Author(s)
ABSTRACT
This paper argues that "following the science” is not always the best strategy. It does so by examining the first phase of the coronavirus disease 2019 (COVID-19) pandemic in three countries: Denmark, the Netherlands, and Sweden. All three countries possessed highly respected infectious disease agencies with wide stakeholder involvement. Despite this, Danish, Dutch, and Swedish public health agencies underplayed the threat of the COVID-19 virus, discouraged intrusive mitigation measures, and were slow to admit their mistakes. Countries that trusted their national agencies, specifically the Netherlands and Sweden, witnessed higher mortality. By contrast, the Danish government marginalized its epidemiologists and suppressed the spread of the virus. The paper thus demonstrates the limits of trusting national scientific expertise, even when properly embedded within social networks, during periods of heightened uncertainty. © 2023 Policy Studies Organization.
ABSTRACT
This paper analyzes the discourses produced in two digital newspapers about the link between expert knowledge and the State during the first year of the coronavirus pandemic in Argentina. It explores the treatment that the media constructed about the configuration of technical-political elites, the discourses that are legitimized by them and the disputes and tensions present in this process. It focuses on contents, sources, actors and assessment. The data are elaborated on the basis of discourse analysis, taking into account the complex relationships between the text (structures of journalistic discourse) and its context of production (socio-historical conditions). The results obtained point out the importance of the presence of the role of expert knowledge in the media, as an element that guides the discussion and provides legitimacy to the communication of the measures taken during the pandemic, the focus of the discussion around the measures and the tensions that emerge in it with respect to the expert knowledge protagonists and the reproduction of the biomedical model. Finally, the need for comprehensive responses in strategies that take into account the psychosocial-economic dimension and the social particularities of the scenarios to which they are oriented is discussed.
ABSTRACT
Preventing student dropout is a challenge for higher education institutions (HEIs) that have worsened with COVID-19 and online classes. Despite several research attempts to understand and reduce dropout rates in HEIs, the solutions found in the literature are often hardcoded, making reuse difficult and therefore slowing progress in the area. In an effort to advance the area, this paper introduces a novel portable approach based on genetic algorithms to automatically select the optimal subset of features for dropout prediction in HEIs. Our approach is validated on a dataset containing approx. 248k student records from a Brazilian university. The results show that the proposed approach significantly increases the accuracy in dropout prediction, outperforming previous work in the literature. Our contributions in this paper are fourfold: the implementation of a (i) novel efficient and accurate automatic feature selector that does not require expert knowledge;(ii) an adaptive deep learning model for dropout prediction in sequential data sets;(iii) a portable solution that can be applied to other data sets/degrees;and, (iv) an analysis and discussion of the performance of feature selection and predictive models for dropout prediction. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Reasonable people agree that whenever possible, we ought to rely on experts to tell us what is true or what the best course of action is. But which experts should we rely on and with regard to what issues? Here, I discuss several dangers that accompany reliance on experts, the most important one of which is this: positions that are offered as expert opinion frequently contain elements outside an expert’s domain of expertise, for instance, values not intrinsic to the given domain. I also talk about the practical implications of accepting my view.
ABSTRACT
Creating a public understanding of the dynamics of a pandemic, such as COVID-19, is vital for introducing restrictive regulations. Gathering diverse data responsibly and sharing it with experts and citizens in a timely manner is challenging. This article reviews methodologies of COVID-19 dashboard design and discusses both technical and non-technical challenges associated. Advice and lessons learned from building a citizen-focused, automated county-precision dashboard for Germany are shared. Within four months, the web-based tool had 5 million unique visitors and 70 million sessions. Three developers set up the basic version in less than one week. Early on, data was screen scraped. An iterative process improved timeliness by adding more fine-grained data sources. A collaborative online table editor enabled near real-time corrections. Alerting was setup for errors, and statistics apply for sanity checking. Static site generation and a content delivery network help to serve large user loads in a timely manner. The flexible design allowed to iteratively integrate more complex statistics based on expert knowledge built on top of the collected data and secondary data sources such as ICU beds and citizen movement. © 2020 Owner/Author.