Search results for 'scientific models' (try it on Scholar)

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  1. Gabriele Contessa (2010). Scientific Models and Fictional Objects. Synthese 172 (2):215 - 229.
    In this paper, I distinguish scientific models in three kinds on the basis of their ontological status—material models, mathematical models and fictional models, and develop and defend an account of fictional models as fictional objects—i.e. abstract objects that stand for possible concrete objects.
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  2.  45
    S. Ducheyne (2008). Towards an Ontology of Scientific Models. Metaphysica 9 (1):119-127.
    Scientific models occupy centre stage in scientific practice. Correspondingly, in recent literature in the philosophy of science, scientific models have been a focus of research. However, little attention has been paid so far to the ontology of scientific models. In this essay, I attempt to clarify the issues involved in formulating an informatively rich ontology of scientific models. Although no full-blown theory—containing all ontological issues involved—is provided, I make several distinctions and (...)
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    Silvia De Bianchi (forthcoming). Which Explanatory Role for Mathematics in Scientific Models? Reply to “The Explanatory Dispensability of Idealizations”. Synthese:1-15.
    In The Explanatory Dispensability of Idealizations, Sam Baron suggests a possible strategy enabling the indispensability argument to break the symmetry between mathematical claims and idealization assumptions in scientific models. Baron’s distinction between mathematical and non- mathematical idealization, I claim, is in need of a more compelling criterion, because in scientific models idealization assumptions are expressed through mathematical claims. In this paper I argue that this mutual dependence of idealization and mathematics cannot be read in terms of (...)
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  4. Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.
    Scientific models invariably involve some degree of idealization, abstraction, or nationalization of their target system. Nonetheless, I argue that there are circumstances under which such false models can offer genuine scientific explanations. After reviewing three different proposals in the literature for how models can explain, I shall introduce a more general account of what I call model explanations, which specify the conditions under which models can be counted as explanatory. I shall illustrate this new (...)
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  5. Ronald Giere (2010). An Agent-Based Conception of Models and Scientific Representation. Synthese 172 (2):269–281.
    I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology (...)
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  6.  15
    David Ludwig (2013). Mediating Objects. Scientific and Public Functions of Models in Nineteenth-Century Biology. History and Philosophy of the Life Sciences 35 (2).
    The aim of this article is to examine the scientific and public functions of two- andthree-dimensional models in the context of three episodes from nineteenth-century biology. Iargue that these models incorporate both data and theory by presenting theoretical assumptions inthe light of concrete data or organizing data through theoretical assumptions. Despite their diverseroles in scientific practice, they all can be characterized as mediators between data and theory.Furthermore, I argue that these different mediating functions often reflect their (...)
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  7. Rasmus Grønfeldt Winther (2006). On the Dangers of Making Scientific Models Ontologically Independent: Taking Richard Levins' Warnings Seriously. Biology and Philosophy 21 (5):703-724.
    Levins and Lewontin have contributed significantly to our philosophical understanding of the structures, processes, and purposes of biological mathematical theorizing and modeling. Here I explore their separate and joint pleas to avoid making abstract and ideal scientific models ontologically independent by confusing or conflating our scientific models and the world. I differentiate two views of theorizing and modeling, orthodox and dialectical, in order to examine Levins and Lewontin’s, among others, advocacy of the latter view. I compare (...)
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  8. Daniela M. Bailer-Jones (2013). Scientific Models in Philosophy of Science. University of Pittsburgh Press.
    Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In _Scientific Models in Philosophy of Science, _Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take, and how they are put to use. She examines early mechanical models employed by nineteenth-century (...)
     
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  9. Daniela M. Bailer-Jones (2003). When Scientific Models Represent. International Studies in the Philosophy of Science 17 (1):59 – 74.
    Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the (...)
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  10. Daniela M. Bailer-Jones (2002). Scientists' Thoughts on Scientific Models. Perspectives on Science 10 (3):275-301.
    : This paper contains the analysis of nine interviews with UK scientists on the topic of scientific models. Scientific models are an important, very controversially discussed topic in philosophy of science. A reasonable expectation is that philosophical conceptions of models ought to be in agreement with scientific practice. Questioning practicing scientists on their use of and views on models provides material against which philosophical positions can be measured.
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  11. Ronald N. Giere, Why Scientific Models Are Not Works of Fiction.
    The usual question, “Are models fictions?” is replaced by the question, “Should scientific models be regarded as works of fiction?” This makes it clear that the issue is not one of definition but of interpretation. First one must distinguish between the ontology of scientific models and their function in the practice of science. Theoretical models and works of fiction are ontologically on a par, their both being creations of human imagination. It is their differing (...)
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  12.  7
    Pericle Salvini, Edoardo Datteri, Cecilia Laschi & Paolo Dario (2008). Scientific Models and Ethical Issues in Hybrid Bionic Systems Research. AI and Society 22 (3):431-448.
    Research on hybrid bionic systems (HBSs) is still in its infancy but promising results have already been achieved in laboratories. Experiments on humans and animals show that artificial devices can be controlled by neural signals. These results suggest that HBS technologies can be employed to restore sensorimotor functionalities in disabled and elderly people. At the same time, HBS research raises ethical concerns related to possible exogenous and endogenous limitations to human autonomy and freedom. The analysis of these concerns requires reflecting (...)
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  13.  5
    Armin W. Schulz (2015). The Heuristic Defense of Scientific Models: An Incentive-Based Assessment. Perspectives on Science 23 (4):424-442.
    It is undeniable that much scientific work is model-based. Despite this, the justification for this reliance on models is still controversial. A particular difficulty here is the fact that many scientific models are based on assumptions that do not describe the exact details of many or even any empirical situations very well. This raises the question of why it is that, despite their frequent lack of descriptive accuracy, employing models is scientifically useful.One..
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  14.  92
    Tarja Knuuttila (2011). Scientific Models in Philosophy of Science. International Studies in the Philosophy of Science 24 (4):437-440.
    Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In Scientific Models in Philosophy of Science, Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take (ranging from equations to animals; from physical objects to theoretical constructs), and how they are put to (...)
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  15. Demetris P. Portides (2005). Scientific Models and the Semantic View of Scientific Theories. Philosophy of Science 72 (5):1287-1298.
    I argue against the conception of scientific models advocated by the proponents of the Semantic View of scientific theories. Part of the paper is devoted to clarifying the important features of the scientific modeling view that the Semantic conception entails. The liquid drop model of nuclear structure is analyzed in conjunction with the particular auxiliary hypothesis that is the guiding force behind its construction and it is argued that it does not meet the necessary features to (...)
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  16.  69
    Stephen M. Downes (2011). Scientific Models. Philosophy Compass 6 (11):757-764.
    This contribution provides an assessment of the epistemological role of scientific models. The prevalent view that all scientific models are representations of the world is rejected. This view points to a unified way of resolving epistemic issues for scientific models. The emerging consensus in philosophy of science that models have many different epistemic roles in science is presented and defended.
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  17.  6
    Cory Travers Lewis & Christopher Belanger (2015). The Generality of Scientific Models: A Measure Theoretic Approach. Synthese 192 (1):269-285.
    Scientific models are often said to be more or less general depending on how many cases they cover. In this paper we argue that the cardinality of cases is insufficient as a metric of generality, and we present a novel account based on measure theory. This account overcomes several problems with the cardinality approach, and additionally provides some insight into the nature of assessments of generality. Specifically, measure theory affords a natural and quantitative way of describing local spaces (...)
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  18.  23
    Christopher D. Green (2001). Scientific Models, Connectionist Networks, and Cognitive Science. Philosophical Explorations.
    The employment of a particular class of computer programs known as "connectionist networks" to model mental processes is a widespread approach to research in cognitive science these days. Little has been written, however, on the precise connection that is thought to hold between such programs and actual in vivo cognitive processes such that the former can be said to "model" the latter in a scientific sense. What is more, this relation can be shown to be problematic. In this paper (...)
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  19.  6
    Elisabet Sahtouris (2006). Why True Globalization Depends on New Scientific Models. World Futures 62 (1 & 2):17 – 27.
    Ervin Laszlo's Science and the Akashic Field is vital to our transition from a long epoch of empire building - of the drive to control Earth's resources by fierce competition in a situation of perceived scarcity - to a future of truly cooperative global family. Laszlo's universe is a far cry from the one Western science has taught us and compatible with my own views as a "post-Darwinian" evolution biologist. In fact, no small number of Western scientists today have defected (...)
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  20. Daniela M. Bailer-Jones (2009). Scientific Models in Philosophy of Science. University of Pittsburgh Press.
    Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In _Scientific Models in Philosophy of Science, _Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take, and how they are put to use. She examines early mechanical models employed by nineteenth-century physicists such (...)
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  21. Gabriele Contessa (2007). Representing Reality: The Ontology of Scientific Models and Their Representational Function. Dissertation, University of London
    Today most philosophers of science believe that models play a central role in science and that one of the main functions of scientific models is to represent systems in the world. Despite much talk of models and representation, however, it is not yet clear what representation in this context amounts to nor what conditions a certain model needs to meet in order to be a representation of a certain system. In this thesis, I address these two (...)
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  22. William E. Herfel (1990). Coming Attractions: Chaos and Complexity in Scientific Models. Dissertation, Temple University
    Chaos, once considered antithetical to scientific law and order, is presently the subject of a vigorous and progressive scientific research program. "Chaos" as it is used in current scientific literature is a technical term: it refers to stochastic behavior generated by deterministic systems. This behavior has appeared in models of a wide range of phenomena including atmospheric patterns, population dynamics, celestial motion, heartbeat rhythms, turbulent fluids, chemical reactions and social structures. In general, chaos arises in the (...)
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  23.  46
    Roman Frigg & Stephan Hartmann (2005). Scientific Models. In Sahotra Sarkar et al (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge
    Models are of central importance in many scientific contexts. The roles the MIT bag model of the nucleon, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka- Volterra model of predator-prey interaction, agent-based and evolutionary models of social interaction, or general equilibrium models of markets play in their respective domains are cases in point.
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  24.  42
    Axel Gelfert (2011). Scientific Models, Simulation, and the Experimenter's Regress. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge
    According to the "experimenter's regress", disputes about the validity of experimental results cannot be closed by objective facts because no conclusive criteria other than the outcome of the experiment itself exist for deciding whether the experimental apparatus was functioning properly or not. Given the frequent characterization of simulations as "computer experiments", one might worry that an analogous regress arises for computer simulations. The present paper analyzes the most likely scenarios where one might expect such a "simulationist's regress" to surface, and, (...)
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  25.  15
    Adam Toon (2012). Models as Make-Believe: Imagination, Fiction, and Scientific Representation. Palgrave Macmillan.
    Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.
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  26. Richard David-Rus (2011). Explanation Through Scientific Models: Reframing the Explanation Topic. Logos and Episteme 2 (2):177-189.
     
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  27.  21
    Chuang Liu, Fictionalism, Realism, Empiricism on Scientific Models.
    This paper defends an approach to modeling and models in science that is against model fictionalism of a recent stripe (the “new fictionalism” that takes models to be abstract entities that are analogous to works of fiction). It further argues that there is a version of fictionalism on models to which my approach is neutral and which only makes sense if one adopts a special sort of antirealism (e.g. constructive empiricism). Otherwise, my approach strongly suggests that one (...)
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  28.  33
    Décio Krause, Jonas Arenhart & Fernando Moraes (2011). Axiomatization and Models of Scientific Theories. Foundations of Science 16 (4):363-382.
    In this paper we discuss two approaches to the axiomatization of scientific theories in the context of the so called semantic approach, according to which (roughly) a theory can be seen as a class of models. The two approaches are associated respectively to Suppes’ and to da Costa and Chuaqui’s works. We argue that theories can be developed both in a way more akin to the usual mathematical practice (Suppes), in an informal set theoretical environment, writing the (...)
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  29.  27
    Jeroen Van Bouwel (2015). Towards Democratic Models of Science. Exploring the Case of Scientific Pluralism. Perspectives on Science 23 (2):149-172.
    Scientific pluralism, a normative endorsement of the plurality or multiplicity of research approaches in science, has recently been advocated by several philosophers (e.g., Kellert et al. 2006, Kitcher 2001, Longino 2013, Mitchell 2009, and Chang 2010). Comparing these accounts of scientific pluralism, one will encounter quite some variation. We want to clarify the different interpretations of scientific pluralism by showing how they incarnate different models of democracy, stipulating the desired interaction among the plurality of research approaches (...)
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  30.  32
    Jack C. Carloye (1971). An Interpretation of Scientific Models Involving Analogies. Philosophy of Science 38 (4):562-569.
    In order to account for the actual function of analogue models in extending theories to new domains, we argue that it is necessary to analyze the inference involved into a complex two dimensional form. This form must go horizontally from descriptions of entities used as a model to redescriptions of entities in the new domain, and it must go vertically from an observation language to a theoretical language having a different and exclusive logical syntax. This complex inference can only (...)
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  31.  55
    Collin Rice (2015). Moving Beyond Causes: Optimality Models and Scientific Explanation. Noûs 49 (3):589-615.
    A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system. Furthermore, in many contexts, it is in (...)
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  32. Gabriele Contessa (2011). Scientific Models and Representation. In Steven French & Juha Saatsi (eds.), The Continuum Companion to the Philosophy of Science. Continuum Press 120--137.
    My two daughters would love to go tobogganing down the hill by themselves, but they are just toddlers and I am an apprehensive parent, so, before letting them do so, I want to ensure that the toboggan won’t go too fast. But how fast will it go? One way to try to answer this question would be to tackle the problem head on. Since my daughters and their toboggan are initially at rest, according to classical mechanics, their final velocity will (...)
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  33.  17
    Eric Hochstein (2013). Intentional Models as Essential Scientific Tools. International Studies in the Philosophy of Science 27 (2):199-217.
    In this article, I argue that the use of scientific models that attribute intentional content to complex systems bears a striking similarity to the way in which statistical descriptions are used. To demonstrate this, I compare and contrast an intentional model with a statistical model, and argue that key similarities between the two give us compelling reasons to consider both as a type of phenomenological model. I then demonstrate how intentional descriptions play an important role in scientific (...)
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  34.  18
    Ronald Giere (2009). Why Scientific Models Should Not Be Regarded as Works of Fiction. In Mauricio Suárez (ed.), Fictions in Science: Philosophical Essays on Modeling and Idealization. Routledge 248--258.
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  35.  23
    Henk W. De Regt (2005). Scientific Realism in Action: Molecular Models and Boltzmann's Bildtheorie. [REVIEW] Erkenntnis 63 (2):205-230.
    This paper approaches the scientific realism question from a naturalistic perspective. On the basis of a historical case study of the work of James Clerk Maxwell and Ludwig Boltzmann on the kinetic theory of gases, it shows that scientists’ views about the epistemological status of theories and models typically interact with their scientific results. Subsequently, the implications of this result for the current realism debate are analysed. The case study supports Giere’s moderately realist view of scientific (...)
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  36. Gabriele Contessa (2006). Scientific Models, Partial Structures and the New Received View of Theories. [REVIEW] Studies in History and Philosophy of Science Part A 37 (2):370-377.
  37.  21
    Wendy S. Parker (2010). Scientific Models and Adequacy-for-Purpose. Modern Schoolman 87 (3-4):285-293.
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  38.  38
    Michael Ruse (1973). The Nature of Scientific Models : Formal V Material Analogy. Philosophy of the Social Sciences 3 (1):63-80.
  39.  15
    Brand Blanshard & On Philosophical Style (2009). Badiou, Alain, Theory of the Subject, London and New York: Continuum, 2009, Pp. Xliv+ 367,£ 22.99. Bailer-Jones, Daniela M., Scientific Models in Philosophy of Science, Pittsburgh: University of Pittsburgh Press, 2009, Pp. X+ 235, $45.00. Baofu, Peter, The Future of Post-Human Martial Arts: A Preface to a New Theory of The. [REVIEW] Mind 118:472.
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  40.  9
    Stephan Hartmann & Roman Frigg (2005). Scientific Models. In SarkarSahotra (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge
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  41.  18
    Mohd Hazim Shah bin Abdul Murad (2011). Models, Scientific Realism, the Intelligibility of Nature, and Their Cultural Significance. Studies in History and Philosophy of Science Part A 42 (2):253-261.
    In this article, I will view realist and non-realist accounts of scientific models within the larger context of the cultural significance of scientific knowledge. I begin by looking at the historical context and origins of the problem of scientific realism, and claim that it is originally of cultural and not only philosophical, significance. The cultural significance of debates on the epistemological status of scientific models is then related to the question of ‘intelligibility’ and how (...)
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  42.  27
    Herman Meyer (1951). On the Heuristic Value of Scientific Models. Philosophy of Science 18 (2):111-123.
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  43.  26
    Henk W. Regt (2005). Scientific Realism in Action: Molecular Models and Boltzmann's Bildtheorie. Erkenntnis 63 (2):205 - 230.
    This paper approaches the scientific realism question from a naturalistic perspective. On the basis of a historical case study of the work of James Clerk Maxwell and Ludwig Boltzmann on the kinetic theory of gases, it shows that scientists’ views about the epistemological status of theories and models typically interact with their scientific results. Subsequently, the implications of this result for the current realism debate are analysed. The case study supports Giere’s moderately realist view of scientific (...)
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  44. Henry Harris (ed.) (1979). Scientific Models and Man. Oxford University Press.
     
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  45.  3
    Anna-Mari Rusanen & Otto Lappi (2012). An Information Semantic Account of Scientific Models. In Henk W. de Regt (ed.), Epsa Philosophy of Science: Amsterdam 2009. Springer 315--327.
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  46.  3
    Stuart Peterfreund (1994). Scientific Models in Optics: From Metaphor to Metonymy and Back. Journal of the History of Ideas 55:59-73.
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  47.  3
    Metaphysics Theta & John Dunn (2010). Abbott, Edwin Abbott. 2010. Flatland. Cambridge: Cambridge University Press. Ix+ 294 Pp. Altman, Andrew. 2009. A Liberal Theory of International Justice. Oxford: Oxford University Press. 233 Pp. Bailer-Jones, Daniela. 2009. Scientific Models in Philosophy of Science. Pittsburgh. [REVIEW] Philosophical Review 119 (3).
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  48.  9
    Kevin D. Hoover, Identity, Structure, and Causal Representation in Scientific Models.
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  49.  1
    Are Atheists (2010). Bailer-Jones, Daniela M. Scientific Models in Philosophy of Science, University of Pittsburgh Press, 2009, 248 Pp. Blackell, Mark, John Duncan, and Simon Kow, Eds. Rousseau and Desire, University of Toronto Press, 2009, 206 Pp. Blackford, Russell, and Udo Schuklenk. 50 Voices of Disbelief: Why We. [REVIEW] Metaphilosophy 41 (3):0026-1068.
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  50. Tarja Knuuttila (2005). Questioning External and Internal Representation The Case of Scientific Models Tarja Knuuttila and Timo Honkela. In L. Magnani & R. Dossena (eds.), Computing, Philosophy and Cognition. 4--209.
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