Theoretical Virtues in Scientific Practice: An Empirical Study

Theoretical virtues play an important role in the acceptance and belief of theories in science and philosophy. Philosophers have well-developed views on which virtues ought and ought not to influence one’s acceptance and belief. But what do scientists think? This paper presents the results of a quantitative study with scientists from the natural and social sciences and compares their views to those held by philosophers. Some of the more surprising results are: (i) all three groups have a preference order regarding theoretical virtues, making theory choice a much more determinate matter than what has sometimes been suggested; (ii) the preference orders are very similar for the three groups; (iii) simplicity is viewed as an epistemic virtue particularly by social scientists (but not by philosophers); and (iv) syntactic parsimony is preferred to ontological parsimony by all three groups.

Cognitive values like simplicity, broad scope, and easy handling are properties of a scientific representation that result from the idealization which is involved in the construction of a representation. These properties may facilitate the application of epistemic values to credibility assessments, which provides a rationale for assigning an auxiliary function to cognitive values. In this paper, I defend a further rationale for cognitive values which consists in the assessment of the usefulness of a representation. Usefulness includes the relevance of a representation regarding the investigation of a given problem and its practicability for the users. This rationale builds on the claim that any evaluation of scientific representations should pursue two aims: providing information about their credibility and providing information about their usefulness. Cognitive values relating to the usefulness of a representation and epistemic values relating to its credibility both perform a first-order function. Cognitive values are abstract, and several values with first-order functions may conflict in their application. Thus, in order for cognitive values to account for the sort of problem that is to be investigated by means of a representation, they need to be appropriately specified and weighed. Comprehensiveness, complexity, high resolution, and easy handling, for instance, may be required in a first-order function for model-based prediction of regional climate impacts but not for explaining how the global climate system works. Specifying and weighing cognitive and epistemic values relative to a given problem is a legitimate second-order function of social values.

This paper presents the results of an empirical study following up on Mizrahi (2021). Using the same methods of text mining and corpus analysis used by Mizrahi (2021), we test empirically a philosophical account of scientific progress that Mizrahi (2021) left out of his empirical study, namely, the so-called functional-internalist account of scientific progress according to which the aim or goal or scientific research is to solve problems. In general, our results do not lend much empirical evidence in support of the problem-solving model of scientific progress over the other philosophical accounts of scientific progress (namely, the epistemic, noetic, and semantic accounts of scientific progress) tested in Mizrahi (2021) and in this follow-up study. Of all the subjects in the JSTOR database we have tested in this study, however, Mathematics is an interesting exception as far as the problem-solving model of scientific progress is concerned. For, in Mathematics alone, we have found that there is significantly more talk of the aims and/or goals of research in terms of solutions than in terms of truth, knowledge, or understanding.

Sustainability has become a central concern globally, and efforts to enhance it are being made across various fields. In line with this trend, corporate sustainability reports have become more widely published. These reports provide both financial and non-financial information on a company’s sustainability. In this context, this study aims to, first, analyze the key keywords contained in CEO messages. Second, it examines whether the keywords emphasized by CEOs change in response to shifts in corporate risk under economic uncertainty. Finally, it identifies how the categories of words included in these messages are classified. To address these research questions, text analysis was selected as the methodology. Specifically, a qualitative research approach using text mining and CONCOR analysis was conducted on the text from sustainability report. According to the Term Frequency and Term Frequency-Inverse Document Frequency analyses, the most frequently occurring keywords were ESG, Sustainable, Society, Stakeholders, Growth, Environment, Effort, and Future. Centrality analysis identified the following keywords as having high centrality: Sustainable, ESG, Society, Environment, Growth, Effort, and Stakeholders. Finally, CONCOR analysis revealed four clusters: Eco-friendly Energy, ESG Management, Global Crisis, and Technological Competitiveness. This study is significant in that it analyzes the major keywords and their changes within unstructured text data using text mining and CONCOR analysis, and it suggests the possibility of future quantitative analysis of non-financial information using these keywords.

Misunderstanding of phenomenology and positivism as perspectives upon the philosophy and practice of science has important implications for the future of geographic research and research funding in America. Positivistic perspectives on the philosophy of science appear to be seen as fundable, whereas the scientific status and the possibilities for funding of phenomenological and other perspectives on the philosophy of science seem to be in question.

This scoping review examines the integration of artificial intelligence (AI) tools into scientific education practices in school settings. Following the PRISMA statement guidelines, a literature search was conducted in the Web of Science and Scopus databases, identifying 2892 articles published between 2020 and 2024. After applying the eligibility criteria, 75 primary studies with empirical data demonstrating the outcomes of school‐based scientific practices using AI tools were selected. The studies were coded based on the type of AI, the type of scientific practice, AI competencies, and other contextual and pedagogical aspects. The results indicate that the most frequently used tools by students are computer vision, natural language processing and data mining. Most studies focus on secondary education levels, combining strategies such as project‐based learning, scientific inquiry and the STEM approach. Although the evidence suggests that these tools enhance scientific skills like data interpretation and computational thinking, limitations were identified, including the use of low‐reliability or paid software and a lack of representation of ethical competencies. Finally, the review highlights the need to strengthen interdisciplinary collaboration and the design of curricular programmes that integrate AI through an ethical framework. The findings are discussed to provide guidance for pedagogy, public policies and future research. Context and implications Rationale for this study: This study was conducted to examine current evidence on how AI tools are being integrated into K‐12 scientific practices. Why the new findings matter: The findings presented are relevant for analysing current trends, identifying research gaps and exploring opportunities to update interdisciplinary pedagogical frameworks and digital literacy curriculum policies. Implications for educational practitioners, policy makers and researchers: For education practitioners, this study provides a planning model with practical examples for integrating AI into K‐12 scientific practices. For policy makers, it highlights the urgency of updating curricular frameworks and public education policies to ensure equitable access to trustworthy technologies, as well as initial and ongoing teacher training in AI, while fostering interdisciplinary collaboration, particularly between science and mathematics. For researchers, there is a clear need to expand empirical studies that examine scientific practices in virtual and hybrid modalities, in underexplored levels such as elementary education, and that take into account ethical competencies in the use of AI.

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis, the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.

Notes on Contributors

BackgroundScience teachers' understanding of the roles of computing practices in science frame how they enact scientific computational practices in their teaching and how their students perceive the relationship between computational practices and scientific endeavours.ObjectivesThis critical, integrative review synthesizes teacher learning literature about the role of computational literacy and computing practices in K‐12 science teaching.MethodsWe examined 54 peer‐reviewed articles and analysed the ways the researchers and teacher participants describe the affordances of integrating computational thinking (CT) and other computational practices in science. We characterize how CT and computational practices are framed in relation to scientific learning goals. We identify six primary affordances for integrating computational practices with science that are conveyed to teachers and by teachers, as represented in these studies of teacher learning.Results and ConclusionsThese six perspectives include (1) learning computer science principles, (2) developing CT dispositions, (3) engagement and inclusion in science, (4) taking ownership of science, (5) supporting learning science content, and (6) participating in computational practice as a form of scientific epistemic practice. Our analysis indicates that computational thinking and computational practices are often integrated in science in order to teach something about computing (e.g., Perspective 1), rather than to support learners' scientific work. Only the 29 articles coded for the sixth perspective—that is, in service of epistemic aims in science—demonstrate commitment to students' uses of computational ideas and practices as epistemic tools to participate in the sensemaking work of science.TakeawaysComparison of Perspectives 5 and 6 illustrates the nuance between computational practices in science that reify something students have already “figured out,” rather than those that serve epistemic goals. Perspective 6 encapsulates the deep synergy among (1) the reflexive nature of computing with scientific ideas and (2) computing as a central practice in science and engineering. We contend that a more focused message of computational practices in service of scientific sensemaking goals is necessary if we expect teachers to enact CT and related computational practices in their classrooms.

Metabolic Syndrome and Nutrition: Text Mining and Network Analysis Using Abstracts of PubMed

Recent developments in the philosophy of science, particularly those falling under the rubric of “scientific realism,” have earned growing recognition among theorists of international relations but have failed to generate substantive programs of research. Consequently, the empirical relevance of much philosophical discourse, such as that centering on the agent-structure problem in social theory, remains unestablished. This article attempts to bridge the gap between the philosophy and practice of science by outlining a model of international structure based on the principles of scientific realism and by considering its implications for a structural research program in international relations theory. Appealing to Imre Lakatos's methodology of theorychoice, the article presents an ontological case for adopting a “transformational” model of structure over the “positional” model developed in the work of Kenneth Waltz. The article demonstrates that the positional approach offers no conceptual or explanatory hold on those features of the international structure that are the intended products of state action. In conclusion, the article argues that the stakes in the agent-structure debate include the capacity to generate integrative structural theory and the ability to theorize the possibilities for peaceful change in the international system.

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the role of technologies and instruments in the process of knowledge production. In this exercise, it becomes evident that technologies cannot be analyzed on their own, but always in relation to epistemic agents. Thus, Techno-Scientific Practices emphasizes the importance of analyzing the process of knowledge production in techno-scientific contexts, in which there is a triad of relations to look at: us, the instruments, and the world. The book thus builds bridges between the philosophy of science, philosophy of technology, and science and technology studies in an unprecedent way.

In scholarly debates, as well as in everyday parlance, we tend to pull science and technology apart: science gives us theory, and technology applies it. In practice, however, science and technologies are highly intertwined. In Techno-Scientific Practices: An Informational Approach, Federica Russo looks at the practice of science and elucidates the role of technologies and instruments in the process of knowledge production. In this exercise, it becomes evident that technologies cannot be analyzed on their own, but always in relation to epistemic agents. Thus, Techno-Scientific Practices emphasizes the importance of analyzing the process of knowledge production in techno-scientific contexts, in which there is a triad of relations to look at: us, the instruments, and the world. The book thus builds bridges between the philosophy of science, philosophy of technology, and science and technology studies in an unprecedent way.

Notes on Contributors

Straipsnyje analizuojama šiuolaikinių diskusijų mokslo vertybinio neutralumo klausimais priešistorė. Išskiriami trys mokslo vertybinio neutralumo problemos aspektai: loginis (deskriptyvinių teiginių ir vertinimų dichotomija ir „Hume'o principas“), metodologinis (reikalavimas priimti ir atmesti hipotezes vien pažintinių vertybių pagrindu), etinis (draudimas mokslo vardu ginti ir skelbti nepažintines vertybes) ir trys diskusijos šiuo klausimu etapai. Analizuojami neutralumo principo šalininkų argumentai, pateikti pirmų dviejų tos diskusijos etapų metu. Ypatingas dėmesys skiriamas keblumams, su kuriais susidūrė šio principo pagrindimas: tai duomenų statistinio reikšmingumo lygmens pasirinkimo priklausomybės nuo nepažintinių vertybių problema, kuri iškyla tikrinant statistines hipotezes (ji yra aktuali loginiam empirizmui), taip pat problema, kaip suderinti sprendimų ir faktų dichotomiją su bazinių teiginių konvencionalumo teze (su ja susiduria kritinis racionalizmas). Aptariami neomarksistinės „kritinės teorijos“ kritinio socialinio mokslo uždavinių sampratos ypatumai, nulėmę jos ryžtingą opoziciją mokslo vertybinio neutralumo principui. Kaip atskaitos taškas skirtingoms mokslo vertybinio neutralumo principo redakcijoms išdėstyti ir palyginti imamos klasikinės vokiečių sociologo Maxo Weberio (1864-1920) formuluotės. Ypatingas dėmesys skiriamas Weberio teiginiams apie „kultūros mokslų“ vertybinių prielaidų kintamumą, anticipuojantiems kai kurias postpozityvistinės mokslo filosofijos idėjas. Lyginant Weberio pažiūras į racionalią diskusiją dėl „galutinių“ vertinimų su povėberinės analitinės metaetikos idėjomis, kvestionuojamas požiūris (jį reiškia A. Maclntyre ir W. G. Runcimanas), kad Weberis buvo nonkognityvistas. Weberio pasisakymai šiuo klausimu interpretuojami kaip kvazitranscendentalinės praktinio proto dialektikos apmatai, kurie gali būti gretinami su Kanto transcendentaline teorinio proto dialektika.