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  1. Trevor J. M. Bench-Capon & Giovanni Sartor (2003). A Model of Legal Reasoning with Cases Incorporating Theories and Values. Artificial Intelligence 150 (1-2):97-143.
    Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, (...)
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Analogy in Science
  1. Paulo Abrantes (1999). Analogical Reasoning and Modeling in the Sciences. Foundations of Science 4 (3):237-270.
    This paper aims at integrating the work onanalogical reasoning in Cognitive Science into thelong trend of philosophical interest, in this century,in analogical reasoning as a basis for scientificmodeling. In the first part of the paper, threesimulations of analogical reasoning, proposed incognitive science, are presented: Gentner''s StructureMatching Engine, Mitchel''s and Hofstadter''s COPYCATand the Analogical Constraint Mapping Engine, proposedby Holyoak and Thagard. The differences andcontroversial points in these simulations arehighlighted in order to make explicit theirpresuppositions concerning the nature of analogicalreasoning. In the (...)
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  2. Peter Achinstein (1972). Models and Analogies: A Reply to Girill. Philosophy of Science 39 (2):235-240.
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  3. Peter Achinstein (1964). Models, Analogies, and Theories. Philosophy of Science 31 (4):328-350.
    Recent accounts of scientific method suggest that a model, or analogy, for an axiomatized theory is another theory, or postulate set, with an identical calculus. The present paper examines five central theses underlying this position. In the light of examples from physical science it seems necessary to distinguish between models and analogies and to recognize the need for important revisions in the position under study, especially in claims involving an emphasis on logical structure and similarity in form between theory and (...)
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  4. Joseph Agassi (1964). Analogies as Generalizations. Philosophy of Science 31 (4):351-356.
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  5. Theodore Bach (2014). A Unified Account of General Learning Mechanisms and Theory‐of‐Mind Development. Mind and Language 29 (3):351-381.
    Modularity theorists have challenged that there are, or could be, general learning mechanisms that explain theory-of-mind development. In response, supporters of the ‘scientific theory-theory’ account of theory-of-mind development have appealed to children's use of auxiliary hypotheses and probabilistic causal modeling. This article argues that these general learning mechanisms are not sufficient to meet the modularist's challenge. The article then explores an alternative domain-general learning mechanism by proposing that children grasp the concept belief through the progressive alignment of relational structure that (...)
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  6. 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 be (...)
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  7. A. Charles Catania (2000). Metaphors, Models, and Mathematics in the Science of Behavior. Behavioral and Brain Sciences 23 (1):94-95.
    Metaphors and models involve correspondences between events in separate domains. They differ in the form and precision of how the correspondences are expressed. Examples include correspondences between phylogenic and ontogenic selection, and wave and particle metaphors of the mathematics of quantum physics. An implication is that the target article's metaphors of resistance to change may have heuristic advantages over those of momentum.
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  8. Lindley Darden (1982). Artificial Intelligence and Philosophy of Science: Reasoning by Analogy in Theory Construction. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:147 - 165.
    This paper examines the hypothesis that analogies may play a role in the generation of new ideas that are built into new explanatory theories. Methods of theory construction by analogy, by failed analogy, and by modular components from several analogies are discussed. Two different analyses of analogy are contrasted: direct mapping (Mary Hesse) and shared abstraction (Michael Genesereth). The structure of Charles Darwin's theory of natural selection shows various analogical relations. Finally, an "abstraction for selection theories" is shown to be (...)
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  9. Eric Dietrich (2010). Analogical Insight: Toward Unifying Categorization and Analogy. Cognitive Processing 11 (4):331-.
    The purpose of this paper is to present two kinds of analogical representational change, both occurring early in the analogy-making process, and then, using these two kinds of change, to present a model unifying one sort of analogy-making and categorization. The proposed unification rests on three key claims: (1) a certain type of rapid representational abstraction is crucial to making the relevant analogies (this is the first kind of representational change; a computer model is presented that demonstrates this kind of (...)
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  10. Eric Dietrich (2008). Toward Extending the Relational Priming Model: Six Questions. Behavioral and Brain Sciences 31 (4):383-384.
    Six questions are posed that are really specific versions of this question: How can Leech et al.'s system be extended to handle adult-level analogies that frequently combine concepts from semantically distant domains sharing few relational labels and that involve the production of abstractions? It is Leech et al. who stress development; finding such an extension would seem to have to be high on their priority list.
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  11. Eric Dietrich (2000). Analogy and Conceptual Change, or You Can't Step Into the Same Mind Twice. In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual change in humans and machines. Lawrence Erlbaum. 265--294.
    Sometimes analogy researchers talk as if the freshness of an experience of analogy resides solely in seeing that something is like something else -- seeing that the atom is like a solar system, that heat is like flowing water, that paint brushes work like pumps, or that electricity is like a teeming crowd. But analogy is more than this. Analogy isn't just seeing that the atom is like a solar system; rather, it is seeing something new about the atom, an (...)
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  12. Eric Dietrich, Arthur B. Markman & Michael Winkley (2003). The Prepared Mind: The Role of Representational Change in Chance Discovery. In Yukio Ohsawa Peter McBurney (ed.), Chance Discovery by Machines. Springer-Verlag, pp. 208-230..
    Analogical reminding in humans and machines is a great source for chance discoveries because analogical reminding can produce representational change and thereby produce insights. Here, we present a new kind of representational change associated with analogical reminding called packing. We derived the algorithm in part from human data we have on packing. Here, we explain packing and its role in analogy making, and then present a computer model of packing in a micro-domain. We conclude that packing is likely used in (...)
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  13. Yves Gingras & Alexandre Guay (2011). The Uses of Analogies in Seventeenth and Eighteenth Century Science. Perspectives on Science 19 (2):154-191.
    The uses of analogy are ancient. It can even be argued that analogical thinking is the most basic cognitive tool humans have to move from the unknown to the known (Gentner et al. 2001). As Olson succinctly puts it, “analogies are useful when it is desired to compare an unfamiliar system with one that is better known” (Olson 1943, p. i). Analogical thinking is thus ubiquitous and found in many texts at least since Homer in Antiquity (Lloyd 1966). For example, (...)
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  14. T. R. Girill (1972). Analogies and Models Revisited. Philosophy of Science 39 (2):241-244.
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  15. R. Harre (1988). Where Models and Analogies Really Count. International Studies in the Philosophy of Science 2 (2):118 – 133.
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  16. Rom Harré (2004). Modeling: Gateway to the Unknown: A Work. Elsevier.
    Edited by Daniel Rothbart of George Mason University in Virginia, this book is a collection of Rom Harré's work on modeling in science (particularly physics and psychology). In over 28 authored books and 240 articles and book chapters, Rom Harré of Georgetown University in Washington, DC is a towering figure in philosophy, linguistics, and social psychology. He has inspired a generation of scholars, both for the ways in which his research is carried out and his profound insights. For Harré, the (...)
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  17. Mary B. Hesse (1966). Models and Analogies in Science. University of Notre Dame Press.
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  18. Mary B. Hesse (1953). Models in Physics. British Journal for the Philosophy of Science 4 (15):198-214.
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  19. Jürgen Hollatz (1999). Analogy Making in Legal Reasoning with Neural Networks and Fuzzy Logic. Artificial Intelligence and Law 7 (2-3):289-301.
    Analogy making from examples is a central task in intelligent system behavior. A lot of real world problems involve analogy making and generalization. Research investigates these questions by building computer models of human thinking concepts. These concepts can be divided into high level approaches as used in cognitive science and low level models as used in neural networks. Applications range over the spectrum of recognition, categorization and analogy reasoning. A major part of legal reasoning could be formally interpreted as an (...)
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  20. Xiaoxi Huang, Huaxin Huang, Beishui Liao & Cihua Xu (2013). An Ontology-Based Approach to Metaphor Cognitive Computation. Minds and Machines 23 (1):105-121.
    Language understanding is one of the most important characteristics for human beings. As a pervasive phenomenon in natural language, metaphor is not only an essential thinking approach, but also an ingredient in human conceptual system. Many of our ways of thinking and experiences are virtually represented metaphorically. With the development of the cognitive research on metaphor, it is urgent to formulate a computational model for metaphor understanding based on the cognitive mechanism, especially with the view to promoting natural language understanding. (...)
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  21. Tokuyasu Kakuta, Makoto Haraguchi & Yoshiaki Okubo (1997). A Goal-Dependent Abstraction for Legal Reasoning by Analogy. Artificial Intelligence and Law 5 (1-2):97-118.
    This paper presents a new algorithm to find an appropriate similarityunder which we apply legal rules analogically. Since there may exist a lotof similarities between the premises of rule and a case in inquiry, we haveto select an appropriate similarity that is relevant to both thelegal rule and a top goal of our legal reasoning. For this purpose, a newcriterion to distinguish the appropriate similarities from the others isproposed and tested. The criterion is based on Goal-DependentAbstraction (GDA) to select a (...)
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  22. Ehud Lamm (2008). Hopeful Heretic – Richard Goldschmidt’s Genetic Metaphors. History and Philosophy of the Life Sciences 30 (3-4):387-406.
    Richard Goldschmidt famously rejected the notion of atomic and corpuscular genes, arranged on the chromosome like beads-on-a-string. I provide an exegesis of Goldschmidt’s intuition by analyzing his repeated and extensive use of metaphorical language and analogies in his attempts to convey his notion of the nature of the genetic material and specifically the significance of chromosomal pattern. The paper concentrates on Goldschmidt’s use of metaphors in publications spanning 1940-1955. -/- .
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  23. W. H. Leatherdale (1974). The Role of Analogy, Model, and Metaphor in Science. American Elsevier Pub. Co..
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  24. Chuang Liu, Fictional Models in Science.
    In this paper, I begin with a discussion of Giere’s recent work arguing against taking models as works of fiction. I then move on to explore a spectrum of scientific models that goes from the obviously fictional to the not so obviously fictional. And then I discuss the modeling of the unobservable and make a case for the idea that despite difficulties of defining them, unobservable systems are modeled in a fundamentally different way than the observable systems. While idealization and (...)
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  25. Mohan P. Matthen (1997). Teleology and the Product Analogy. Australasian Journal of Philosophy 75 (1):21 – 37.
    This article presents an analogical account of the meaning of function attributions in biology. To say that something has a function analogizes it with an artifact, but since the analogy rests on a necessary (but possibly insufficient) basis, function statements can still be assessed as true or false in an objective sense.
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  26. D. H. Mellor (1968). Models and Analogies in Science. Isis 59:282-90.
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  27. Nicola Mößner (2013). Das Beste aus zwei Welten? Ludwik Fleck über den sozialen Ursprung wissenschaftlicher Kreativität. In Philipp Hubmann & Till Julian Huss (eds.), Simultaneität - Modelle der Gleichzeitigkeit in den Wissenschaften und Künsten. transcript.
  28. María G. Navarro (2006). Analogía, Prudencia y Abducción En la Racionalidad Interpretativa. In Mauricio Beuchot (ed.), Contextos de la hermenéutica analógica. Editorial Torres Asociados.
  29. Darrell P. Rowbottom (2011). The Instrumentalist's New Clothes. Philosophy of Science 78 (5):1200-1211.
    This paper develops a new version of instrumentalism, in light of progress in the realism debate in recent decades, and thereby defends the view that instrumentalism remains a viable philosophical position on science. The key idea is that talk of unobservable objects should be taken literally only when those objects are assigned properties (or described in terms of analogies involving things) with which we are experientially (or otherwise) acquainted. This is derivative from the instrumentalist tradition in so far as the (...)
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  30. Michael Ruse (1973). The Nature of Scientific Models : Formal V Material Analogy. Philosophy of the Social Sciences 3 (1):63-80.
  31. Eric Steinhart (2001). The Logic of Metaphor: Analogous Parts of Possible Worlds. Kluwer Academic.
    The Logic of Metaphor uses techniques from possible worlds semantics to provide formal truth-conditions for many grammatical classes of metaphors. It gives logically precise and practically useful syntactic and semantic rules for generating and interpreting metaphors. These rules are implemented in a working computer program. The book treats the lexicon as a conceptual network with semantics provided by an intensional predicate calculus. It gives rules for finding analogies in such networks. It shows how to syntactically and semantically analyze texts containing (...)
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  32. Michael Weisberg (2013). Simulation and Similarity: Using Models to Understand the World. Oxford University Press.
    one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...
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Approximation
  1. Chuang Liu (2004). Approximations, Idealizations, and Models in Statistical Mechanics. Erkenntnis 60 (2):235-263.
    In this paper, a criticism of the traditional theories of approximation and idealization is given as a summary of previous works. After identifying the real purpose and measure of idealization in the practice of science, it is argued that the best way to characterize idealization is not to formulate a logical model – something analogous to Hempel's D-N model for explanation – but to study its different guises in the praxis of science. A case study of it is then made (...)
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  2. Chuang Liu (2004). Approximations, Idealizations, and Models in Statistical Mechanics. Erkenntnis 60 (2):235-263.
    In this paper, a criticism of the traditional theories of approximation and idealization is given. After identifying the real purpose and measure of idealization in the practice of science, it is argued that the best way to characterize idealization is not to formulate a logical model -- something analogous to Hempel's D-N model for explanation -- but to study its different guises in the praxis of science. A case study of it is then made in thermostatistical physics. After a brief (...)
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  3. Sebastian Lutz, Generalizing Empirical Adequacy II: Partial Structures.
    The companion piece to this article captures and generalizes empirical adequacy in terms of vagueness sets. In this article, I show that previous attempts to capture and generalize empirical adequacy in terms of partial structures fail. Indeed, the motivations for the partial structures approach are better met by vagueness sets, which can be used to generalize the partial structure approach.
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  4. Sebastian Lutz (2014). Generalizing Empirical Adequacy I: Multiplicity and Approximation. Synthese 191 (14):3195-3225.
    I provide an explicit formulation of empirical adequacy, the central concept of constructive empiricism, and point out a number of problems. Based on one of the inspirations for empirical adequacy, I generalize the notion of a theory to avoid implausible presumptions about the relation of theoretical concepts and observations, and generalize empirical adequacy with the help of approximation sets to allow for lack of knowledge, approximations, and successive gain of knowledge and precision. As a test case, I provide an application (...)
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  5. John Matthewson & Michael Weisberg (2009). The Structure of Tradeoffs in Model Building. Synthese 170 (1):169 - 190.
    Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss (...)
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  6. Robert Northcott (2013). Degree of Explanation. Synthese 190 (15):3087-3105.
    Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures (...)
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  7. Darrell P. Rowbottom (2011). Approximations, Idealizations and 'Experiments' at the Physics-Biology Interface. Studies in History and Philosophy of Biological and Biomedical Sciences 42 (2):145-154.
    This paper, which is based on recent empirical research at the University of Leeds, the University of Edinburgh, and the University of Bristol, presents two difficulties which arise when condensed matter physicists interact with molecular biologists: (1) the former use models which appear to be too coarse-grained, approximate and/or idealized to serve a useful scientific purpose to the latter; and (2) the latter have a rather narrower view of what counts as an experiment, particularly when it comes to computer simulations, (...)
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  8. Michael J. Shaffer (forthcoming). Approximate Truth, Quasi-Factivity and Evidence. Acta Analytica.
    The main question addressed in this paper is whether some false sentences can constitute evidence for the truth of other propositions. In this paper it is argued that there are good reasons to suspect that at least some false propositions can (at least some times) constitute evidence for the truth of certain other contingent propositions. The paper also introduces a novel condition concerning propositions that constitute evidence that explains a ubiquitous evidential practice and it contains a defense of a particular (...)
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  9. Michael J. Shaffer (2013). E Does Not Equal K. The Reasoner 7:30-31.
    This paper challenges Williamson's "E = K" thesis on the basis of evidential practice. The main point is that most evidence is only approximately true and so cannot be known if knowledge is factive.
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  10. Michael J. Shaffer (2012). Moorean Sentences and the Norm of Assertion. Logos and Episteme 3:653-658.
    In this paper Timothy Williamson’s argument that the knowledge norm of assertion is the best explanation of the unassertability of Morrean sentences is challenged and an alternative account of the norm of assertion is defended.
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  11. Michael J. Shaffer (2012). Not-Exact-Truths, Pragmatic Encroachment and the Epistemic Norm of Practical Reasoning. Logos and Episteme 3:239-259.
    Recently a number of variously motivated epistemologists have argued that knowledge is closely tied to practical matters. On the one hand, radical pragmatic encroachment is the view that facts about whether an agent has knowledge depend on practical factors and this is coupled to the view that there is an important connection between knowledge and action. On the other hand, one can argue for the less radical thesis only that there is an important connection between knowledge and practical reasoning. So, (...)
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  12. Michael J. Shaffer (2011). The Ramsey Principle and The Principle of Informational Equilibrium. The Reasoner 5 (3):37-39.
    This paper challenges the soundness of an argument given in support of a Ramseyan analysis of belief defended by Dokic and Engel in their 2001 book.
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  13. Michael Weisberg (2013). Simulation and Similarity: Using Models to Understand the World. Oxford University Press.
    one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...
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Idealization
  1. Alfonso Arroyo-Santos & Xavier de Donato-Rodríguez, Idealization and the Structure of Theories in Biololgy.
    In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual conditionals that can exhibit different degrees of contingency. We use the idea of possible worlds to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, it is this structure what helps explain why idealizations, despite being deformations of reality, are so successful (...)
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  2. Robert Batterman (2010). On the Explanatory Role of Mathematics in Empirical Science. British Journal for the Philosophy of Science 61 (1):1-25.
    This paper examines contemporary attempts to explicate the explanatory role of mathematics in the physical sciences. Most such approaches involve developing so-called mapping accounts of the relationships between the physical world and mathematical structures. The paper argues that the use of idealizations in physical theorizing poses serious difficulties for such mapping accounts. A new approach to the applicability of mathematics is proposed.
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  3. Robert W. Batterman (2009). Idealization and Modeling. Synthese 169 (3):427 - 446.
    This paper examines the role of mathematical idealization in describing and explaining various features of the world. It examines two cases: first, briefly, the modeling of shock formation using the idealization of the continuum. Second, and in more detail, the breaking of droplets from the points of view of both analytic fluid mechanics and molecular dynamical simulations at the nano-level. It argues that the continuum idealizations are explanatorily ineliminable and that a full understanding of certain physical phenomena cannot be obtained (...)
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  4. Nancy Cartwright (1995). False Idealisation: A Philosophical Threat to Scientific Method. Philosophical Studies 77 (2-3):339 - 352.
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