Modifica di PaginaEntry (sezione)

==Interdisciplinarity==
A superficial view might suggest a conflict between the disciplinary rigidity of the ''<nowiki/>'Physical Paradigm of Science''<nowiki/>' {{Tooltip||2=The "Physical Paradigm of Science" describes a prevailing epistemological approach in the physical sciences, centered on deterministic models and rigorous experimental methodologies. This paradigm relies on empirical observations and the scientific method to seek universal laws governing natural phenomena.'''Key Characteristics'''1. Determinism: Assumes that natural phenomena follow fixed laws, allowing accurate predictions based on initial conditions. 2. ''Measurability and Reproducibility'': Emphasizes quantitative measurements and reproducible experiments to confirm results in different contexts. 3. ''Isolation of Variables'': Focuses on analyzing specific effects by isolating variables, often idealizing systems under controlled conditions. While effective in classical natural sciences, the physical paradigm has limitations in complex fields like neurophysiology, where dynamic interactions and variability challenge deterministic models. '''Application in Masticatory Neurophysiology''': In masticatory neurophysiology, the physical paradigm helps develop basic models but fails to explain emergent behaviors, such as motor unit recruitment in response to complex stimuli. '''Towards an Integrated Paradigm''': Emerging is an "Engineering Paradigm of Science," offering a more adaptive approach that considers complexity, allowing more flexible predictive models that account for non-linear interactions in biological systems}} and the systemic openness of the Engineering Paradigm of Science {{Tooltip||2=The '''Engineering Paradigm of Science''' emphasizes practical applications, interdisciplinary collaboration, and understanding complex systems. It contrasts with traditional deterministic models, focusing instead on solving real-world problems, particularly in fields like biology, medicine, and social sciences.'''Key Characteristics''' ''Problem-Solving Orientation'': Prioritizes solutions to complex issues over purely theoretical models. ''Interdisciplinary Collaboration'': Encourages integrating knowledge from various disciplines, enhancing understanding through shared experiences. ''Focus on Complex Systems'': Recognizes emergent behavior and the interconnectedness of system components, acknowledging that outcomes can be unpredictable and non-linear. ''Iterative Process'': Embraces an adaptive approach, refining models based on empirical data and feedback to improve responsiveness.'''Technological Integration''': Applies engineering principles to enhance research design and data analysis, utilizing simulations and computational modeling. '''Application in Masticatory Neurophysiology''' In masticatory neurophysiology, this paradigm promotes innovative diagnostic tools and therapeutic approaches. By integrating neurophysiology, biomechanics, and materials science, it provides a comprehensive view of jaw function and dysfunction. The Engineering Paradigm of Science fosters collaboration and innovation, ultimately leading to advances that improve our understanding of complex systems and enhance practical outcomes in various fields.}}

📘 According to an important European study,{{Tooltip|<sup>[14]</sup>|<ref>{{cita libro
| autore = Boon M
| autore2 = Van Baalen S
| titolo = Epistemology for interdisciplinary research – shifting philosophical paradigms of science
| url = https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6383598/
| opera = Eur J Philos Sci
| anno = 2019
| DOI = 10.1007/s13194-018-0242-4
}}</ref>|<small> 📌 In scientific policies, it is generally recognized that problem-solving based on science requires interdisciplinary research. 📌 However, the epistemological processes leading to effective interdisciplinary research are still poorly understood. 🧭 This article aims to outline an epistemology of interdisciplinary research (IDR), particularly for solving "real-world" problems. The focus is on why researchers encounter cognitive and epistemic difficulties in conducting interdisciplinary activities. Based on a study of educational literature, it is concluded that higher education lacks clear ideas about the epistemology of interdisciplinary research and, consequently, how to teach it. It is hypothesized that the lack of philosophical attention to the epistemology of IDR is due to the predominance of a philosophical paradigm of science, defined as the "physical paradigm of science," which hinders the recognition of the deep epistemological challenges of interdisciplinarity both in the philosophy of science and in scientific education and research.🧠 An alternative philosophical paradigm, defined as the "engineering paradigm of science," is therefore proposed, which involves different assumptions regarding aspects such as the purpose of science, the nature of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological tools. According to this engineering paradigm, the production of knowledge for epistemic purposes becomes the goal of science, and "knowledge" (theories, models, laws, concepts) is interpreted as an epistemic tool useful for performing cognitive tasks by epistemic agents, rather than as an objective representation of aspects of the world independent of the way it is constructed. This implies that knowledge is inevitably shaped by the way it is constructed. Moreover, the way different scientific disciplines construct knowledge is guided by the specificities of the discipline itself, analyzable through disciplinary perspectives. 🧠 It follows that knowledge and its epistemic uses cannot be understood without at least some understanding of how it is constructed. Consequently, scientific researchers need so-called "metacognitive scaffolding" to assist them in analyzing and reconstructing the processes of knowledge construction and the differences between disciplines. In the engineering paradigm, these metacognitive scaffolding are also interpreted as epistemic tools, but in this case, tools that guide, enable, and limit the analysis and articulation of knowledge production processes (i.e., explain the epistemological aspects of doing research). In interdisciplinary research, such metacognitive scaffolding assist interdisciplinary communication, with the aim of analyzing and articulating how each discipline constructs its own knowledge.</small>|}}

* interdisciplinarity requires:

* metacognitive tools ("cognitive scaffolds")
* common languages between different disciplines
* flexible epistemological models

Another study proposes an engineering interpretation of knowledge{{Tooltip|<sup>[15]</sup>|<ref>{{cita libro
| autore = Boon M
| titolo = An engineering paradigm in the biomedical sciences: Knowledge as epistemic tool
| url = https://www.ncbi.nlm.nih.gov/pubmed/28389261
| opera = Prog Biophys Mol Biol
| anno = 2017
| DOI = 10.1016/j.pbiomolbio.2017.04.001
}}</ref>|<small>📌 To address the complexity of biological systems and attempt to generate applicable results, current biomedical sciences are adopting concepts and methods from engineering sciences. Philosophers of science have interpreted this phenomenon as the emergence of an engineering paradigm, particularly in systems biology and synthetic biology. This article aims to articulate the presumed engineering paradigm in contrast to the physical paradigm that supported the rise of biochemistry and molecular biology. This articulation starts from Kuhn's notion of "disciplinary matrix," which indicates what constitutes a paradigm. It is argued that the core of the physical paradigm lies in its metaphysical and ontological assumptions, while the core of the engineering paradigm consists of the epistemic goal of producing knowledge useful for solving problems external to scientific practice. 🧠 Therefore, the two paradigms imply distinct notions of knowledge. While the physical paradigm involves a representational notion of knowledge, the engineering paradigm implies the notion of "knowledge as an epistemic tool"</small>.}} in biomedical contexts: here, knowledge is considered 'an active tool' for solving complex clinical problems, rather than a mere theoretical representation of reality.