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Abstract

In order to deal with the complexity of biological systems and attempts to generate applicable results, current biomedical sciences are adopting concepts and methods from the engineering sciences. Philosophers of science have interpreted this as the emergence of an engineering paradigm, in particular in systems biology and synthetic biology. This article aims at the articulation of the supposed engineering paradigm by contrast with the physics paradigm that supported the rise of biochemistry and molecular biology. This articulation starts from Kuhn's notion of a disciplinary matrix, which indicates what constitutes a paradigm. It is argued that the core of the physics paradigm is its metaphysical and ontological presuppositions, whereas the core of the engineering paradigm is the epistemic aim of producing useful knowledge for solving problems external to the scientific practice. Therefore, the two paradigms involve distinct notions of knowledge. Whereas the physics paradigm entails a representational notion of knowledge, the engineering paradigm involves the notion of 'knowledge as epistemic tool'.
... Exercise is one method that has been found to improve the prognosis of numerous cancer patients and increase their overall QOL [3][4][5]. The World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), the American Cancer Society (ACS), and the National Cancer Institute (NCI) all recommend that cancer survivors maintain adequate levels of physical activity after treatment to maintain their overall health [6][7][8][9]. Previous studies have also outlined the critical role of exercise as a "prescription" for various chronic noncommunicable diseases [10][11][12]. ...
... They are also being applied within the natural science, humanities, and societal concerns. In the context of scientometrics, methods for data and information visualization-as based on Thomas Samuel Kuhn's paradigm [7][8][9], Derek John de Solla Price's s scientific frontier theory, Bo Ronald S. Burt's structural hole theory [17], Kleinberg's burst detection technology, the optimal foraging theory (OFT) [18][19][20][21], and Chinese knowledge unit discrete and reorganization theory [22]-have been produced and developed by CiteSpace [23,24] and other citation visualization analysis software. With the rapid growth of contemporary scientific research and published literature, visual analysis software provides a strong convenience for scientific researchers to use existing data to refine and extract new knowledge. ...
... Source: IARC data visualization exploration [1,39,40]. 8 BioMed Research International number of publications. Our examination into keywords with citation bursts showcased that "rehabilitation medicine," "activities of daily living," "lung neoplasm," "implementation," "hospice," "exercise oncology," "mental health," "telemedicine," and "multidisciplinary" may be potential directions for future research. ...
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This study analyzed the research hotspots and frontiers of exercise rehabilitation among cancer patients via CiteSpace. Relevant literature published in the core collection of the Web of Science (WoS) database from January 1, 2000, to February 6, 2022, was searched. Further, we used CiteSpace5.8R1 to generate a network map and identified top authors, institutions, countries, keywords, and research trends. A total of 2706 related literature were retrieved. The most prolific writer was found to be Kathryn H Schmitz (21 articles). The University of Toronto (64 articles) was found to be the leading institution, with the United States being the leading country. Further, “rehabilitation,” “exercise,” “quality of life,” “cancer,” and “physical activity” were the top 5 keywords based on frequency; next, “disability,” “survival,” “fatigue,” “cancer,” and “rehabilitation” were the top 5 keywords based on centrality. The keyword “fatigue” was ranked at the top of the most cited list. Finally, “rehabilitation medicine,” “activities of daily living,” “lung neoplasm,” “implementation,” “hospice,” “exercise oncology,” “mental health,” “telemedicine,” and “multidisciplinary” are potential topics for future research. Our results show that the research hotspots have changed from “quality of life,” “survival,” “rehabilitation,” “exercise,” “cancer,” “physical therapy,” “fatigue,” and “breast cancer” to “exercise oncology,” “COVID-19,” “rehabilitation medicine,” “inpatient rehabilitation,” “implementation,” “telemedicine,” “lung neoplasm,” “telehealth,” “multidisciplinary,” “psycho-oncology,” “hospice,” “adapted physical activity,” “cancer-related symptom,” “cognitive function,” and “behavior maintenance.” Future research should explore the recommended dosage and intensity of exercise in cancer patients. Further, following promotion of the concept of multidisciplinary cooperation and the rapid development of Internet medical care, a large amount of patient data has been accumulated; thus, how to effectively use this data to generate results of high clinical value is a question for future researchers.
... The second part of our thesis is that an alternative philosophical view of science can be based on Kuhn's idea of disciplinary matrices (Kuhn 1970), in particular, on an expanded version of elements constituting the matrix. This expanded matrix has been used to articulate two different philosophical visions of science, called a physics paradigm of science versus an engineering paradigm of science (Boon 2017a). Articulating these paradigms was intended to interpret the changing character of the biomedical sciences such as systems biology as compared to, for instance, classical biochemistry -and it was found that the engineering paradigm of science suits better to systems biology than the traditional physics paradigm. ...
... A more appropriate alternative epistemology comes from an engineering paradigm of science. An outline will be given of the extended Kuhnian matrix that is used as a conceptual framework to analyze philosophical views of science, and of the two philosophical paradigms that result from using this matrix as a framework to articulate philosophical views of science (Boon 2017a). Central is the difference between science considered as a unified hierarchy, or network of theories (in a physics paradigm), versus 'knowledge' considered as epistemic tools constructed and shaped within scientific disciplines, where epistemic tools must be constructed such as to be suitable for being used as epistemic resources in performing epistemic tasks in which epistemic results are generated for specific purposes in an ever ongoing scientific research processes (in an engineering paradigm). ...
... At stake are two different philosophical views on science, one focusing on scientific theories for the sake of science, 12 the other focusing on scientific knowledge (in the sense of epistemic resources and results, see Terminology) and epistemic strategies for solving real-world problems. Boon (2017a) has argued that these two views can be analyzed in terms of two distinct philosophical paradigms of science. ...
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In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research (IDR), in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based on a study of educational literature it is concluded that higher-education is missing clear ideas on the epistemology of IDR, and as a consequence, on how to teach it. It is conjectured that the lack of philosophical interest in the epistemology of IDR is due to a philosophical paradigm of science (called a physics paradigm of science), which prevents recognizing severe epistemological challenges of IDR, both in the philosophy of science as well as in science education and research. The proposed alternative philosophical paradigm (called an engineering paradigm of science) entails alternative philosophical presuppositions regarding aspects such as the aim of science, the character of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological instruments. This alternative philosophical paradigm assume the production of knowledge for epistemic functions as the aim of science, and interprets ‘knowledge’ (such as theories, models, laws, and concepts) as epistemic tools that must allow for conducting epistemic tasks by epistemic agents, rather than interpreting knowledge as representations that objectively represent aspects of the world independent of the way in which it was constructed. The engineering paradigm of science involves that knowledge is indelibly shaped by how it is constructed. Additionally, the way in which scientific disciplines (or fields) construct knowledge is guided by the specificities of the discipline, which can be analyzed in terms of disciplinary perspectives. This implies that knowledge and the epistemic uses of knowledge cannot be understood without at least some understanding of how the knowledge is constructed. Accordingly, scientific researchers need so-called metacognitive scaffolds to assist in analyzing and reconstructing how ‘knowledge’ is constructed and how different disciplines do this differently. In an engineering paradigm of science, these metacognitive scaffolds can also be interpreted as epistemic tools, but in this case as tools that guide, enable and constrain analyzing and articulating how knowledge is produced (i.e., explaining epistemological aspects of doing research). In interdisciplinary research, metacognitive scaffolds assist interdisciplinary communication aiming to analyze and articulate how the discipline constructs knowledge.
... In a recent article, I argue that a so-called physics paradigm of science prevents us from recognizing engineering science as a scientific practice. As an alternative, I propose an engineering paradigm of science (Boon 2017b). ...
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This chapter aims to address some salient aspects of the engineering sciences and their methodology in scientific research, cumulating in a methodology of scientific modeling in the engineering sciences. A noticeable difference between scientific research in the engineering sciences by comparison with scientific research in the basic sciences is the role and character of phenomena, which in the basic sciences serve as aids in discovering and testing theories, while the engineering sciences analyze (physical-technological) phenomena in view of technological functioning or malfunctioning. Scientific research on technological problem-solving and innovation, therefore, is better cast in terms of design-concepts that are based on functional interpretations of phenomena. This also has consequences for the ways in which (physical-technological) phenomena are investigated and on the specific character of scientific knowledge for creating or controlling them by means of physical-technological circumstances. Scientific modeling of technological systems is central to the engineering sciences, encompassing both the modeling of physical-technological phenomena in specific physical-technological contexts as well as the modeling of technological artifacts producing specific phenomena. A methodology is proposed for how scientific models of (physical or physical-technological) phenomena are constructed, which is on par with the well-known hypothetical-deductive methodology.
... Currently, most environmental scientists deal with the complexity of eco-social systems and generate applicable results by adopting concepts and methods from the engineering sciences that have the epistemic objective of producing useful knowledge for solving problems external to scientific practice. This is interpreted by philosophers of science as the emergence of an "engineering paradigm" [125]. Interdisciplinarity is acknowledged in science-based problem-solving research (engineering) and transdisciplinarity is acknowledged in resilience research [126]. ...
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Economic and environmental interventions in the Anthropocene have created disruptions that are threatening the capacity of socio-ecological systems to recover from adversities and to be able to maintain key functions for preserving resilience. The authors of this paper underscore the benefits of a workshop-based methodology for developing a vision and an approach to the inner processes of creation that can be used to increase resilience, to cope with societal vulnerabilities and to develop the tools for future planning at local, regional and global scales. Diverse areas of discourse ranging from climate science and sustainability, to psychoanalysis, linguistics and eco-philosophy, contributed meaningfully to the transdisciplinary approach for enhancing resilience. A framework is proposed that can be used throughout society, that integrates the importance of human subjectivity and the variability of human contexts, especially gender, in shaping human experiences and responses to climate change impacts and challenges such as the covid-19 pandemic. Within the domain of socio-economic research, the authors challenge researchers and policy makers to expand future perspectives of resilience through the proposed systemic resilience vision. Movement towards transformative thinking and actions requires inner exploration and visualization of desirable futures for integrating ecological, social, cultural, ethical, and economic dimensions as agencies for catalyzing the transition to livable, sustainable, equitable, ethical, and resilient societies.
... One may even defend that the aim of science is not useful theories, but true theories. Science may be of epistemic and practical value to all kinds of applications such as in engineering and medicine, but this is a by-product of science, not its intended aim (also see Boon 2011Boon , 2017c. Rather, science has an intrinsic cultural value in telling us what the world is like, which is a task that cannot be replaced by machine learning technologies whatsoever since incomprehensive, opaque data-models do not tell us anything meaningful about the world. ...
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This chapter aims to contribute to critically investigate whether human-made scientific knowledge and the scientist’s role in developing it, will remain crucial—or can data-models automatically generated by machine-learning technologies replace scientific knowledge produced by humans? Influential opinion-makers claim that the human role in science will be taken over by machines. Chris Anderson’s (2008) provocative essay, The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, will be taken as an exemplary expression of this opinion. The claim that machines will replace human scientists can be investigated within several perspectives (e.g., ethical, ethical-epistemological, practical and technical). This chapter focuses on epistemological aspects concerning ideas and beliefs about scientific knowledge. The approach is to point out epistemological views supporting the idea that machines can replace scientists, and to propose a plausible alternative that explains the role of scientists and human-made science, especially in view of the multitude of epistemic tasks in practical uses of knowledge. Whereas philosophical studies into machine learning often focus on reliability and trustworthiness, the focus of this chapter is on the usefulness of knowledge for epistemic tasks. This requires distinguishing between epistemic tasks for which machine learning is useful, versus those that require human scientists. In analyzing Anderson’s claim, a kind of double stroke is made. First, it will be made plausible that the fundamental presuppositions of empiricist epistemologies give reason to believe that machines will ultimately make scientists superfluous. Next, it is argued that empiricist epistemologies are deficient, because neglect the multitude of epistemic tasks for which humans need knowledge that is comprehensible for them. The character of machine learning technology is such that it does not provide such knowledge.
... In addition, by obtaining faster bone remodeling, patient recovery is facilitated in less time. Similarly, as it has been mentioned, the growth in the number of small osteoinductive molecules may represent the next generation of clinical therapies for bone repair and regeneration [18][19][20]. ...
... One may even defend that the aim of science is not useful theories, but true theories. Science may be of epistemic and practical value to all kinds of applications such as in engineering and medicine, but this is a by-product of science, not its intended aim (also see Boon 2011Boon , 2017c. Rather, science has an intrinsic cultural value in telling us what the world is like, which is a task that cannot be replaced by machine learning technologies whatsoever since incomprehensive, opaque data-models do not tell us anything meaningful about the world. ...
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This chapter aims at a contribution to critically investigate whether human-made scientific knowledge and the scientist's role in developing it, will remain crucial-or can data-models automatically generated by machine-learning technologies replace scientific knowledge produced by humans? Influential opinion-makers claim that the human role in science will be taken over by machines. Chris Anderson's (2008) provocative essay, The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, will be taken as an exemplary expression of this opinion. The claim that machines will replace human scientists can be investigated within several perspectives (e.g., ethical, ethical-epistemological, practical and technical). This chapter focuses on epistemological aspects concerning ideas and beliefs about scientific knowledge. The approach is to point out epistemological views supporting the idea that machines can replace scientists, and to propose a plausible alternative that explain the role of scientists and human-made science, especially in view of the multitude of epistemic tasks in practical uses of knowledge. Whereas philosophical studies into machine learning often focus on reliability and trustworthiness, the focus of this chapter is on the usefulness of knowledge for epistemic tasks. This requires to distinguish between epistemic tasks for which machine learning is useful, versus those that require human scientists. In analyzing Anderson's claim, a kind of double stroke is made. First, it will be made plausible that the fundamental presuppositions of empiricist epistemologies give reason to believe that machines will ultimately make scientists superfluous. Next, it is argued that empiricist epistemologies are deficient, because it neglects the multitude of epistemic tasks of and by humans, for which humans need knowledge that is comprehensible for them. The character of machine learning technology is such that it does not provide such knowledge. It will be concluded that machine learning is useful for specific types of epistemic tasks such as prediction, classification, and pattern-recognition, but for many other types of epistemic tasks-such as asking relevant questions, problem-analysis, interpreting problems as of a specific kind, designing interventions, and 'seeing' analogies that help to interpret a problem differently-the production and use of comprehensible scientific knowledge remains crucial.
... Kuhn (1970) expands on Kant's epistemology by claiming that knowledge is not only shaped by concepts 'in the mind,' but also by practical, technological and intellectual aspects that constitute a discipline These other aspects have been historically established by partly contingent, practical, technological and intellectual developments of a specific (scientific) practice. Kuhn's major contribution was to emphasize the indelible role of this 'hidden' background-called paradigms by Kuhn and disciplinary perspectives by us-in the construction of knowledge (Andersen 2013(Andersen , 2016Boon 2017aBoon , 2017bBoon and Van Baalen forthcoming). This philosophical insight thus rejects positivist ideas about (scientific) knowledge even more thoroughly. ...
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Purpose: Clarification of interdisciplinary expertise as the ability to deal with the cognitive and epistemological challenges of multi-and interdisciplinary problem-solving-such as in developing and implementing medical technology for diagnoses and treatment of patients in collaborations between clinicians, technicians, and engineers-and of the higher-order cognitive skills needed as part of this expertise. Method: Clarify the epistemological difficulties of combining scientific knowledge, methodologies and technologies from different disciplines in problem-solving, by drawing on recent developments in the philosophy of science. Conclusion: We argue that interdisciplinary expertise involves the cognitive ability to connect, translate and establish links between disciplinary knowledge, as well as the metacognitive ability to understand and explain the role of the disciplinary perspective-consisting of, e.g. basic concepts, theories, models, methodologies, technologies, and specific ways of measuring , reasoning and modeling in a discipline-in how knowledge is used and produced.
... In a recent article, I argue that a so-called physics paradigm of science prevents us from recognizing engineering science as a scientific practice. As an alternative, I propose an engineering paradigm of science (Boon 2017b). ...
Preprint
Full-text available
This chapter aims to address some salient aspects of the engineering sciences and their methodology in scientific research, cumulating in a methodology of scientific modeling in the engineering sciences. A noticeable difference between scientific research in the engineering sciences by comparison with scientific research in the basic sciences, is the role and character of phenomena, which in the basic sciences serve as aids in discovering and testing theories, while the engineering sciences analyze (physical-technological) phenomena in view of technological functioning or malfunctioning. Scientific research on technological problem-solving and innovation, therefore, is better cast in terms of design-concepts that are based on functional interpretations of phenomena. This also has consequences for the ways in which (physical-technological) phenomena are investigated and on the specific character of scientific knowledge for creating or controlling them by means of physical-technological circumstances. Scientific modeling of technological systems is central to the engineering sciences, encompassing both the modeling of physical-technological phenomena in specific physical-technological contexts as well as the modeling of technological artifacts producing specific phenomena. A methodology is proposed for how scientific models of (physical or physical-technological) phenomena are constructed, which is on par with the well-known hypothetical-deductive methodology. 2
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Purpose: Clarification of interdisciplinary expertise as the ability to deal with the cognitive and epistemological challenges of multi- and interdisciplinary problem-solving —such as in developing and implementing medical technology for diagnoses and treatment of patients in collaborations between clinicians, technicians and engineers—, and of the higher-order cognitive skills needed as part of this expertise. Method: Clarify the epistemological difficulties of combining scientific knowledge, methodologies and technologies from different disciplines in problem-solving, by drawing on recent developments in the philosophy of science. Conclusion: We argue that interdisciplinary expertise involves the cognitive ability to connect, translate and establish links between disciplinary knowledge, as well as the metacognitive ability to understand and explain the role of the disciplinary perspective —consisting of, e.g., basic concepts, theories, models, methodologies, technologies, and specific ways of measuring, reasoning and modeling in a discipline— in how knowledge is used and produced. Key words: interprofessional education, adaptive expertise, interdisciplinary expertise, metacognitive skills, higher-order cognitive abilities, epistemology, problem-solving, reflection, disciplinary perspectives, medical technology.
Technical Report
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The term ‘in silico clinical trials’ refers to: “The use of individualised computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention.” While computer simulation is widely used for the development and de-risking of a number of ‘mission-critical’ products such as civil aircraft, nuclear power plants, etc, biomedical product development and assessment is still predominantly founded on experimental rather than computer-simulated approaches. The need for long and complex experiments in vitro, on animals, and then on patients during clinical trials pushes development costs to unsustainable levels, stifling innovation, and driving the cost of healthcare provision to unprecedented levels. The Avicenna Action, funded by the European Commission, has engaged 525 experts from 35 countries, including 22 of the 28 members of the European Union, in an 18-month consensus process, to produce this research and technological development roadmap. This document provides an overview of how biomedical products are developed today, where in silico clinical trials technologies are already used, and where else they could be used. From the identification of the barriers that prevent wider adoption, we derived a detailed list of research and technological challenges that require pre-competitive funding to be overcome. We recommend that the European Commission, and all other international and national research funding agencies, include these research targets among their priorities, allocating significant resources to support approaches that could result in huge socioeconomic benefit. We also recommend industrial and academic stakeholders explore the formation of a pre-competitive alliance to coordinate and implement public and privately funded research on this topic. Last, but not least, we recommend that regulatory bodies across the world embrace innovation and, in collaboration with academic and industrial experts, develop the framework of standards, protocols, and shared resources required to evaluate the safety and the efficacy of biomedical products using in silico clinical trials technologies.
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Philosophers of science have tended to avoid the problem of “development” by focusing primarily on evolutionary biology and, more recently, on molecular biology and genetics. Jason Scott Robert explores the nature of development as it relates to current concepts in biological theory and practice and analyzes the interrelations between development and evolution (evo-devo), an area of resurgent biological inquiry. © Jason Scott Robert 2004 and Cambridge University Press, 2009.
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With the realization that the grounds upon which an hypothesis comes to be formulated can be considered separately from the grounds upon which it is accepted, it has become popular among some philosophers and scientists to claim that although it may be of psychological interest to understand the genesis of hypotheses, there can be no logic of search or discovery. While it is unclear exactly what is meant by the claim that there can be no logic of search, it is clear that this opinion, coupled with anecdotes of Poincaré’s sudden solution of a mathematical problem while stepping on a Madrid streetcar, and Kekulé’s vision of a snake biting its tail, have left the aura that the generation of an hypothesis is as mysterious as a Gestalt shift in perception of a figure. Perhaps because Gestalt shifts seem to occur without a processes of reasoning, but in some sense, spontaneously, the use of such perceptual shifts as models of hypothesis formation have lent support to the claim that there can be no logic of search. To a practicing scientist, the image of the startling’ shift’ and insight might seem overly flattering of the scientist’s genius, and the actual generation of hypotheses seem more reasonable and less mysterious. Some of the ways in which the generation of an hypothesis is a rather reasonable affair will be discussed below in conjunction with an effort to examine some of the features of what I am calling articulation of parts explanations, as they occur in biology.
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The inapplicability of variations on theory reduction in the context of genetics and their irrelevance to ongoing research has led to an anti-reductionist consensus in philosophy of biology. One response to this situation is to focus on forms of reductive explanation that better correspond to actual scientific reasoning (e.g. part–whole relations). Working from this perspective, we explore three different aspects (intrinsicality, fundamentality, and temporality) that arise from distinct facets of reductive explanation: composition and causation. Concentrating on these aspects generates new forms of reductive explanation and conditions for their success or failure in biology and other sciences. This analysis is illustrated using the case of protein folding in molecular biology, which demonstrates its applicability and relevance, as well as illuminating the complexity of reductive reasoning in a specific biological context. • 1 Introduction • 2 Composition, Causation, and Varieties of Reduction • 2.1 Composition versus causation • 2.2 The Nagelian framework and its aftermath • 3 Part-whole Reduction: Intrinsicality, Fundamentality, and Temporality • 3.1 Intrinsicality and fundamentality • 3.2 Temporality • 3.2.1 Atemporal part-whole reduction • 3.2.2 Temporal (causal) part-whole reduction • 4 The Protein Folding Problem • 4.1 Background and significance • 4.2 Reductive explanation in molecular biology • 5 Philosophical Evaluation • 5.1 Application: intrinsicality and fundamentality • 5.2 Relevance: temporality • 6 Conclusion