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  1. Models and Stories in Hadron Physics.Stephan Hartmann - 1999 - In Mary S. Morgan & Margaret Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press. pp. 52--326.
    Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics is a case in point. This theory is supposed to govern all strong interactions, but it is extremely hard to apply and test at energies where protons, neutrons and ions are the effective degrees of freedom. Instead, scientists typically use highly idealized models such as the MIT Bag Model or the Nambu Jona-Lasinio Model to account for phenomena in (...)
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  • Can the History of Science Contribute to Modelling in Physics Teaching?Juliana Machado & Marco Antônio Barbosa Braga - 2016 - Science & Education 25 (7-8):823-836.
    A characterization of the modelling process in science is proposed for science education, based on Mario Bunge’s ideas about the construction of models in science. Galileo’s Dialogues are analysed as a potentially fruitful starting point to implement strategies aimed at modelling in the classroom in the light of that proposal. It is argued that a modelling process for science education can be conceived as the evolution from phenomenological approaches towards more representational ones, emphasizing the role of abstraction and idealization in (...)
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  • Mario Bunge, Systematic Philosophy and Science Education: An Introduction.Michael R. Matthews - 2012 - Science & Education 21 (10):1393-1403.
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  • Science, Worldviews and Education: An Introduction.Michael R. Matthews - 2009 - Science & Education 18 (6-7):641-666.
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  • Invisible disagreement: an inverted qualia argument for realism.Justin Donhauser - 2017 - Philosophical Studies 174 (3):593-606.
    Scientific realists argue that a good track record of multi-agent, and multiple method, validation of empirical claims is itself evidence that those claims, at least partially and approximately, reflect ways nature actually is independent of the ways we conceptualize it. Constructivists contend that successes in validating empirical claims only suffice to establish that our ways of modelling the world, our “constructions,” are useful and adequate for beings like us. This essay presents a thought experiment in which beings like us intersubjectively (...)
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  • Evaluating Scientific Research Projects: The Units of Science in the Making.Mario Bunge - 2017 - Foundations of Science 22 (3):455-469.
    Original research is of course what scientists are expected to do. Therefore the research project is in many ways the unit of science in the making: it is the center of the professional life of the individual scientist and his coworkers. It is also the means towards the culmination of their specific activities: the original publication they hope to contribute to the scientific literature. The scientific project should therefore be of central interest to all the students of science, particularly the (...)
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  • Mind and Life: Is the Materialist Neo-Darwinian Conception of Nature False?Martin Zwick - 2016 - Biological Theory 11 (1):25-38.
    partial review of Thomas Nagel’s book, Mind and Cosmos: Why the Materialist Neo-Darwinian Conception of Nature Is Almost Certainly False is used to articulate some systems-theoretic ideas about the challenge of understanding subjective experience. The article accepts Nagel’s view that reductionist materialism fails as an approach to this challenge, but argues that seeking an explanation of mind based on emergence is more plausible than seeking one based on pan-psychism, which Nagel favors. However, the article proposes something similar to Nagel’s neutral (...)
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  • Parrying.Kenneth Mark Colby - 1981 - Behavioral and Brain Sciences 4 (4):550-560.
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  • Colby's paranoia model: An old theory in a new frame?C. E. Izard & F. A. Masterson - 1981 - Behavioral and Brain Sciences 4 (4):539-540.
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  • Towards an Ontology of Problems.Martin Zwick - 1995 - Advances in Systems Science and Applications 1:37-42.
    Systems theory offers a language in which one might formulate a metaphysics (or more specifically an ontology) of problems. This proposal is based upon a conception of systems theory shared by vonBertalanffy, Wiener, Boulding, Rapoport, Ashby, Klir, and others,and expressed succinctly by Bunge, who considered game theory, information theory, feedback control theory, and the like to be attempts to construct an "exact and scientific metaphysics." Our prevailing conceptions of "problems" are concretized yet also fragmented, and in fact dissolved, by the (...)
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  • Understanding Imperfection.Martin Zwick - 2000 - In Understanding Imperfection. International Society for the Systems Sciences.
    In this talk, I want to present a conception that I have been working on for a number of years (Zwick, 1983, 1995) about the use of systems ideas. I start from the negative and proceed to the positive. The negative assertion is that systems ideas by themselves , unsupplemented by more specific and concrete knowledge (e.g., from the various disciplines), are insufficient for practical application, either for obtaining knowledge about the world or for solving problems. The reason for this (...)
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  • The Logical vs. the Ontological Understanding of Conditions.Rögnvaldur Ingthorsson - 2008 - Metaphysica 9 (2):129-137.
    According to the truth-functional analysis of conditions, to be ‘necessary for’ and ‘sufficient for’ are converse relations. From this, it follows that to be ‘necessary and sufficient for’ is a symmetric relation, that is, that if P is a necessary and sufficient condition for Q, then Q is a necessary and sufficient condition for P. This view is contrary to common sense. In this paper, I point out that it is also contrary to a widely accepted ontological view of conditions, (...)
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  • Can robots make good models of biological behaviour?Barbara Webb - 2001 - Behavioral and Brain Sciences 24 (6):1033-1050.
    How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which (...)
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  • Idealization in Quantum Field Theory.Stephan Hartmann - 1990 - In Niall Shanks (ed.), Idealization in Contemporary Physics. pp. 99-122.
    This paper explores various functions of idealizations in quantum field theory. To this end it is important to first distinguish between different kinds of theories and models of or inspired by quantum field theory. Idealizations have pragmatic and cognitive functions. Analyzing a case-study from hadron physics, I demonstrate the virtues of studying highly idealized models for exploring the features of theories with an extremely rich structure such as quantum field theory and for gaining some understanding of the physical processes in (...)
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  • An Inverted Qualia Argument for Direct Realism.Justin Donhauser - 2024 - Topoi 43 (1):211-219.
    This essay extends my “invisible disagreement” argument for Color Realism (2017) to formulate an argument for Direct Realism. It uses a variation of an “inverted qualia” thought experiment to show that successes in intersubjectively validating empirical claims about colors is proof that a nuanced version of Direct Realism is correct.
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  • Linguistic Research in the Empirical Paradigm as Outlined by Mario Bunge.Dorota Zielińska - 2022 - Mεtascience: Scientific General Discourse 2:182-202.
    In view of the critique of the methodology of the dominant interdisciplinary re-search involving language studies as the main component, in particular clinical linguistics, Cummings (2014) proposes that “It is perhaps appropriate at this point to move the debate onto non-empirical grounds.” In Cummings (2014: 113) she starts such a debate on the grounds of the philosophy of language and pragmatics. In this article, I propose to expand that debate by including the input of the philosophy of science. I start (...)
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  • What Is Metascientific Ontology?François Maurice - 2022 - Mεtascience: Scientific General Discourse 2:22-44.
    Metascientific ontology differs from philosophical ontologies in its objectives, objects and methods. By an examination of the ontological theories of Mario Bunge, we will show their main objective is a unified representation of the world as known through the sciences, that their objects of study are scientific concepts, and that their methods do not differ from those that one expects to find in any rational activity. Metascientific ontology is therefore not transcendent because it does not seek to represent non-concrete objects (...)
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  • Qu'est-ce que l'ontologie métascientifique?François Maurice - 2022 - Mεtascience: Discours Général Scientifique 2:19-43.
    L’ontologie métascientifique se distingue des ontologies philosophiques par ses objectifs, ses objets et ses méthodes. Par un examen des théories ontologiques de Mario Bunge, nous montrerons que leur principal objectif est l’élaboration d’une représentation unifiée du monde tel que connu via les sciences, que leurs objets d’étude sont les concepts scientifiques, et que leurs méthodes ne diffèrent pas de celles qu’on s’attend à trouver dans toute activité rationnelle. L’ontologie métascientifique n’est donc pas transcendante parce qu’elle ne cherche pas à représenter (...)
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  • Searches for the origins of the epistemological concept of model in mathematics.Gert Schubring - 2017 - Archive for History of Exact Sciences 71 (3):245-278.
    When did the concept of model begin to be used in mathematics? This question appears at first somewhat surprising since “model” is such a standard term now in the discourse on mathematics and “modelling” such a standard activity that it seems to be well established since long. The paper shows that the term— in the intended epistemological meaning—emerged rather recently and tries to reveal in which mathematical contexts it became established. The paper discusses various layers of argumentations and reflections in (...)
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  • Meeting the Discipline-Culture Framework of Physics Knowledge: A Teaching Experience in Italian Secondary School.Olivia Levrini, Eugenio Bertozzi, Marta Gagliardi, Nella Grimellini Tomasini, Barbara Pecori, Giulia Tasquier & Igal Galili - 2014 - Science & Education 23 (9):1701-1731.
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  • Explicatures are NOT Cancellable.Alessandro Capone - 2013 - In Perspectives on Linguistic Pragmatics. Cham: Springer. pp. 131-151.
    Explicatures are not cancellable. Theoretical considerations.
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  • Models in science and in science education: an introduction.Michael R. Matthews - 2007 - Science & Education 16 (7-8):647-652.
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  • Deep and shallow simulations.Aaron Sloman - 1981 - Behavioral and Brain Sciences 4 (4):548-548.
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  • Is PARRY paranoid?David W. Swanson - 1981 - Behavioral and Brain Sciences 4 (4):548-549.
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  • Clinical artificial intelligence.Virginia Teller & Hartvig Dahl - 1981 - Behavioral and Brain Sciences 4 (4):549-550.
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  • The dichotomous predicament of contemporary psychology.V. Pinkava - 1981 - Behavioral and Brain Sciences 4 (4):546-547.
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  • Psychiatry and computers: An uneasy synthesis.William H. Reid & John F. Riedler - 1981 - Behavioral and Brain Sciences 4 (4):547-547.
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  • Evaluation of a model's test.Russell Revlin - 1981 - Behavioral and Brain Sciences 4 (4):547-548.
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  • On the generality of PARRY, Colby's paranoia model.Manfred Kochen - 1981 - Behavioral and Brain Sciences 4 (4):540-541.
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  • How smart must you be to be crazy?Robert Lindsay - 1981 - Behavioral and Brain Sciences 4 (4):541-542.
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  • Colby's model for paranoia: It's made well, but what is it?Peter A. Magaro & Harvey G. Shulman - 1981 - Behavioral and Brain Sciences 4 (4):542-543.
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  • Testing the components of a computer model.Brendan A. Maher - 1981 - Behavioral and Brain Sciences 4 (4):543-543.
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  • PARRY and the evaluation of cognitive models.James R. Miller - 1981 - Behavioral and Brain Sciences 4 (4):543-544.
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  • Al and cargo cult science.James Moor - 1981 - Behavioral and Brain Sciences 4 (4):544-545.
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  • Modeling paranoia: The cargo cult metaphor.Keith Oatley - 1981 - Behavioral and Brain Sciences 4 (4):545-546.
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  • Going after PARRY.Robert P. Abelson - 1981 - Behavioral and Brain Sciences 4 (4):534-535.
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  • Simulation?Joseph Agassi - 1981 - Behavioral and Brain Sciences 4 (4):535-536.
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  • Modeling a paranoid mind.Kenneth Mark Colby - 1981 - Behavioral and Brain Sciences 4 (4):515-534.
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  • Paranoia concerning program-resistant aspects of the mind - and let's drop rocks on Turing's toes again.Keith Gunderson - 1981 - Behavioral and Brain Sciences 4 (4):537-539.
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  • Issues in computer modeling of cognitive phenomena: An artificial intelligence perspective.Jaime G. Carbonell - 1981 - Behavioral and Brain Sciences 4 (4):536-537.
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  • The moral dimension in political assessments of the social impact of technology.Tom Settle - 1976 - Philosophy of the Social Sciences 6 (4):315-334.
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