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  1. Peter Achinstein (ed.) (2004). Science Rules: A Historical Introduction to Scientific Methods. Johns Hopkins University Press.
    Is there a universal set of rules for discovering and testing scientific hypotheses? Since the birth of modern science, philosophers, scientists, and other thinkers have wrestled with this fundamental question of scientific practice. Efforts to devise rigorous methods for obtaining scientific knowledge include the twenty-one rules Descartes proposed in his Rules for the Direction of the Mind and the four rules of reasoning that begin the third book of Newton's Principia , and continue today in debates over the very possibility (...)
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  2. Alan Baker (2008). Complexity Unfavoured. Analysis 68 (297):85–88.
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  3. Greg Bamford (1999). What is the Problem of Ad Hoc Hypotheses? Science and Education 8 (4):375 - 86..
    The received view of an ad hochypothesis is that it accounts for only the observation(s) it was designed to account for, and so non-ad hocness is generally held to be necessary or important for an introduced hypothesis or modification to a theory. Attempts by Popper and several others to convincingly explicate this view, however, prove to be unsuccessful or of doubtful value, and familiar and firmer criteria for evaluating the hypotheses or modified theories so classified are characteristically available. These points (...)
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  4. Peter Baumann (2005). Theory Choice and the Intransitivity of 'Is a Better Theory Than'. Philosophy of Science 72 (1):231-240.
    There is a very plausible principle of the transitivity of justifying reasons. It says that if "p" is better justified than "q" (all things considered) and "q" better than "r", then "p" is better justified than "r" (all things considered). There is a corresponding principle of rational theory choice. Call one theory "a better theory than" another theory if all criteria of theory choice considered (explanatory power, simplicity, empirical adequacy, etc.), the first theory meets the criteria better than the second (...)
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  5. Michael Baumgartner (forthcoming). Detecting Causal Chains in Small-N Data. Field Methods.
    The first part of this paper shows that Qualitative Comparative Analysis (QCA)--also in its most recent forms as presented in Ragin (2000, 2008)--, does not correctly analyze data generated by causal chains, which, after all, are very common among causal processes in the social sciences. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a,b) has introduced a Boolean methodology, termed Coincidence Analysis (CNA), that (...)
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  6. Michael Baumgartner (2009). Uncovering Deterministic Causal Structures: A Boolean Approach. Synthese 170 (1):71 - 96.
    While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic structures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is assigned (...)
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  7. Michael Baumgartner (2009). Uncovering Deterministic Causal Structures: A Boolean Approach. Synthese 170 (1):71-96.
    While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic struc- tures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is (...)
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  8. Gordon Belot (forthcoming). Curve-Fitting for Bayesians? British Journal for the Philosophy of Science:axv061.
    Bayesians often assume, suppose, or conjecture that for any reasonable explication of the notion of simplicity a prior can be designed that will enforce a preference for hypotheses simpler in just that sense. Further, it is often claimed that the Bayesian framework automatically implements Occam’s razor—that conditionalizing on data consistent with both a simple theory and a complex theory more or less inevitably favours the simpler theory. But it is shown here that there are simplicity-driven approaches to curve-fitting problems that (...)
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  9. Bernadette Bensaude-Vincent, Sacha Loeve, Alfred Nordmann & Astrid Schwarz (2011). Matters of Interest: The Objects of Research in Science and Technoscience. [REVIEW] Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 42 (2):365-383.
    This discussion paper proposes that a meaningful distinction between science and technoscience can be found at the level of the objects of research. Both notions intermingle in the attitudes, intentions, programs and projects of researchers and research institutions—that is, on the side of the subjects of research. But the difference between science and technoscience becomes more explicit when research results are presented in particular settings and when the objects of research are exhibited for the specific interest they hold. When an (...)
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  10. Gregor Betz (2010). Warum erfolgreiche Prognosen neuartiger Phänomene methodologisch wertvoll sind. Deutsche Zeitschrift für Philosophie 58 (2):329-332.
  11. Thomas Boyer (2014). Is a Bird in the Hand Worth Two in the Bush? Or, Whether Scientists Should Publish Intermediate Results. Synthese 191 (1):17-35.
    A part of the scientific literature consists of intermediate results within a longer project. Scientists often publish a first result in the course of their work, while aware that they should soon achieve a more advanced result from this preliminary result. Should they follow the proverb “a bird in the hand is worth two in the bush”, and publish any intermediate result they get? This is the normative question addressed in this paper. My aim is to clarify, to refine, and (...)
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  12. Alex Broadbent (2011). Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press 45--69.
    This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their (...)
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  13. Peter Brössel (2013). Assessing Theories: The Coherentist Approach. Erkenntnis 79 (3):593-623.
    In this paper we show that the coherence measures of Olsson (J Philos 94:246–272, 2002), Shogenji (Log Anal 59:338–345, 1999), and Fitelson (Log Anal 63:194–199, 2003) satisfy the two most important adequacy requirements for the purpose of assessing theories. Following Hempel (Synthese 12:439–469, 1960), Levi (Gambling with truth, New York, A. A. Knopf, 1967), and recently Huber (Synthese 161:89–118, 2008) we require, as minimal or necessary conditions, that adequate assessment functions favor true theories over false theories and true and informative (...)
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  14. Georg Brun (forthcoming). Explication as a Method of Conceptual Re-Engineering. Erkenntnis:1-31.
    Taking Carnap’s classic exposition as a starting point, this paper develops a pragmatic account of the method of explication, defends it against a range of challenges and proposes a detailed recipe for the practice of explicating. It is then argued that confusions are involved in characterizing explications as definitions, and in advocating precising definitions as an alternative to explications. Explication is better characterized as conceptual re-engineering for theoretical purposes, in contrast to conceptual re-engineering for other purposes and improving exactness for (...)
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  15. Chuanfei Chin (2014). Subliming and Subverting: An Impasse on the Contingency of Scientific Rationality. Journal of the Philosophy of History 8 (2):311-331.
    What is special about the philosophy of history when the history is about science? I shall focus on an impasse between two perspectives — one seeking an ideal of rationality to guide scientific practices, and one stressing the contingency of the practices. They disagree on what this contingency means for scientific norms. Their impasse underlies some fractious relations within History and Philosophy of Science. Since the late 1960s, this interdisciplinary field has been (...)
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  16. Daniel Cohnitz (2006). Poor Thought Experiments? A Comment on Peijnenburg and Atkinson. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 37 (2):373 - 392.
    In their paper, 'When are thought experiments poor ones?' (Peijnenburg and Atkinson, 2003, Journal of General Philosophy of Science 34, 305-322), Jeanne Peijnenburg and David Atkinson argue that most, if not all, philosophical thought experiments are "poor" ones with "disastrous consequences" and that they share the property of being poor with some (but not all) scientific thought experiments. Noting that unlike philosophy, the sciences have the resources to avoid the disastrous consequences, Peijnenburg and Atkinson come to the conclusion that the (...)
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  17. Klodian Coko & Jutta Schickore (2013). Robustness, Solidity, and Multiple Determinations. Metascience 22 (3):681-683.
    Review of Soler et al. (eds.) Characterizing the robustness of science: After the practice turn in philosophy of science.
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  18. Richard Corry (2009). How is Scientific Analysis Possible? In Toby Handfield (ed.), Dispositions and Causes. Oxford University Press, Clarendon Press ;
    One of the most powerful tools in science is the analytic method, whereby we seek to understand complex systems by studying simpler sub-systems from which the complex is composed. If this method is to be successful, something about the sub-systems must remain invariant as we move from the relatively isolated conditions in which we study them, to the complex conditions in which we want to put our knowledge to use. This paper asks what this invariant could be. The paper shows (...)
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  19. Josquin Debaz & Sophie Roux (2007). D'une affaire aux autres. In Sophie Roux (ed.), Retours Sur l'Affaire Sokal. Harmattan 1--48.
  20. Tamás Demeter (2012). Hume's Experimental Method. British Journal for the History of Philosophy 20 (3):577-599.
    In this article I attempt to reconstruct David Hume's use of the label ?experimental? to characterise his method in the Treatise. Although its meaning may strike the present-day reader as unusual, such a reconstruction is possible from the background of eighteenth-century practices and concepts of natural inquiry. As I argue, Hume's inquiries into human nature are experimental not primarily because of the way the empirical data he uses are produced, but because of the way those data are theoretically processed. He (...)
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  21. Tamás Demeter & Gábor Á Zemplén (2010). Being Charitable to Scientific Controversies: On the Demonstrativity of Newton's Experimentum Crucis. The Monist 93 (4):640-656.
    Current philosophical reflections on science have departed from mainstream history of science with respect to both methodology and conclusions. The article investigates how different approaches to reconstructing commitments can explain these differences and facilitate a mutual understanding and communication of these two perspectives on science. Translating the differences into problems pertaining to principles of charity, the paper offers a platform for clarification and resolution of the differences between the two perspectives. The outlined contextual approach occupies a middle ground between mainstream (...)
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  22. Yiftach J. H. Fehige (2010). Quantum Physics and Theology. [REVIEW] American Catholic Philosophical Quarterly 84 (1):186-188.
  23. Yiftach J. H. Fehige (2007). Gedankenexperimente. Die Genese Einer Wissenschaftsphilosophischen Forschungstradition Nach Ulrich Kühne. Deutsche Zeitschrift für Philosophie (1):149-157.
    This is a review essay of what is probably the best contribution to the history of the philosophical investigation into thought experiments.
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  24. Yiftach J. H. Fehige & James R. Brown (2010). Thought Experiments. Stanford Encyclopedia of Philosophy 25 (1):135-142.
  25. Nicolas Fillion & Robert M. Corless (2014). On the Epistemological Analysis of Modeling and Computational Error in the Mathematical Sciences. Synthese 191 (7):1451-1467.
    Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe a general model (...)
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  26. Eugen Fischer (2014). Mind the Metaphor! A Systematic Fallacy in Analogical Reasoning. Analysis 75 (1):67-77.
    Conceptual metaphors facilitate both productive and pernicious analogical reasoning. This article addresses the question: When and why does the frequently helpful use of metaphor become pernicious? By applying the most influential theoretical framework from cognitive psychology in analysing the philosophically most prominent example of pernicious metaphorical reasoning, we identify a philosophically relevant but previously undescribed fallacy in analogical reasoning with metaphors. We then outline an explanation of why even competent thinkers commit this fallacy and obtain a psychologically informed ‘debunking’ (...)
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  27. Gabor Forrai (2002). Lakatos, Reason, and Rationality. In G. Kampis L. Kvasz & M. Stöltzner (eds.), Appraising Lakatos: Mathematics, Methodology, and the Man. Kluwer 73-83.
    Lakatos's methodology, if analysed as belonging to the demarcationist-rationalist program launched by Popper gives some interesting conclusions concerning the feasibility of the project: (1) Rationalism cannot provide arguments against relativism. (2) A theory of scientific rationality cannot be defended without relying on scientific authorities. (3) A historical justification of scientific rationality does not show that the procedures that are rational according to the theory are truth-conducive.
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  28. Danny Frederick, A Regimented and Concise Exposition of Karl Popper’s Critical Rationalist Epistemology.
  29. Alexander Gebharter, Addendum to "A Formal Framework for Representing Mechanisms?".
    In (Gebharter 2014) I suggested a framework for modeling the hierarchical organization of mechanisms. In this short addendum I want to highlight some connections of my approach to the statistics and machine learning literature and some of its limitations not mentioned in the paper.
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  30. Alexander Gebharter (2014). A Formal Framework for Representing Mechanisms? Philosophy of Science 81 (1):138-153.
    In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) intervening in some of a mechanism’s parts should have some influence on the (...)
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  31. Alexander Gebharter & Marie I. Kaiser (2014). Causal Graphs and Biological Mechanisms. In Marie I. Kaiser, Oliver Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special sciences: The case of biology and history. Springer 55-85.
    Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...)
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  32. Alexander Gebharter & Gerhard Schurz (2014). Editors' Introduction. Theoria. An International Journal for Theory, History and Foundations of Science 29 (1):5-7.
  33. Pierre-Luc Germain, Emanuele Ratti & Federico Boem (2014). Junk or Functional DNA? ENCODE and the Function Controversy. Biology and Philosophy 29 (6):807-831.
    In its last round of publications in September 2012, the Encyclopedia Of DNA Elements (ENCODE) assigned a biochemical function to most of the human genome, which was taken up by the media as meaning the end of ‘Junk DNA’. This provoked a heated reaction from evolutionary biologists, who among other things claimed that ENCODE adopted a wrong and much too inclusive notion of function, making its dismissal of junk DNA merely rhetorical. We argue that this criticism rests on misunderstandings concerning (...)
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  34. James Goetz (2014). Natural Unity and Paradoxes of Legal Persons. Journal Jurisprudence 21:27-46.
    This essay proposes an ontological model in which a legal person such as a polity possesses natural unity from group properties that emerge in the self-organization of the human population. Also, analysis of customary legal persons and property indicates noncontradictory paradoxes that include Aristotelian essence of an entity, relative identity over time, ubiquitous authority, coinciding authorities, and identical entities. Mathematical modeling helps to explain the logic of the paradoxes.
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  35. Attila Grandpierre (2011). The Biological Principle of Natural Sciences and the Logos of Life of Natural Philosophy: A Comparison and the Perspectives of Unifying the Science and Philosophy of Life. Analecta Husserliana 110 (Part II):711-727.
    Acknowledging that Nature is one unified whole, we expect that physics and biology are intimately related. Keeping in mind that physics became an exact science with which we are already familiar with, while, apparently, we do not have at present a similar knowledge about biology, we consider how can we make useful the clarity of physics to shed light to biology. The next question will be what are the most basic categories of physics and biology. If we do not want (...)
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  36. Stephan Hartmann (1996). The World as a Process: Simulations in the Natural and Social Sciences. In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...)
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  37. Gary Hatfield (2014). Cartesian Psychology of Antoine Le Grand. In Mihnea Dobre & Tammy Nyden (eds.), Cartesian Empiricisms. Springer 251-274.
    In the Aristotelian curriculum, De anima or the study of the soul fell under the rubric of physics. This area of study covered the vital (“vegetative”), sensitive, and rational powers of the soul. Descartes’ substance dualism restricted reason or intellect, and conscious sensation, to human minds. Having denied mind to nonhuman animals, Descartes was required to explain all animal behavior using material mechanisms possessing only the properties of size, shape, position, and motion. Within the framework of certainty provided by the (...)
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  38. Michael Heidelberger (1994). Alternative Interpretationen der Repräsentationstheorie der Messung. In Ulla Wessels & Georg Meggle (eds.), Analyomen / Analyomen: Proceedings of the 1st Conference. De Gruyter 310-323.
    Four different interpretations of measurement are distinguished that are compatible with the formal frame of the representational theory of measurement: (1) the classical interpretation, the additive, (3) the operationalis, (4) the correlative one.
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  39. Conrad Heilmann (2015). A New Interpretation of the Representational Theory of Measurement. Philosophy of Science 82 (5):787-797.
    On the received view, the Representational Theory of Measurement reduces measurement to the numerical representation of empirical relations. This account of measurement has been widely criticized. In this article, I provide a new interpretation of the Representational Theory of Measurement that sidesteps these debates. I propose to view the Representational Theory of Measurement as a library of theorems that investigate the numerical representability of qualitative relations. Such theorems are useful tools for concept formation that, in turn, is one crucial aspect (...)
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  40. Brian Hepburn & Hanne Andersen (2015). Scientific Method. Stanford Encyclopedia of Philosophy.
    1. Overview and organizing themes 2. Historical Review: Aristotle to Mill 3. Logic of method and critical responses 3.1 Logical constructionism and Operationalism 3.2. H-D as a logic of confirmation 3.3. Popper and falsificationism 3.4 Meta-methodology and the end of method 4. Statistical methods for hypothesis testing 5. Method in Practice 5.1 Creative and exploratory practices 5.2 Computer methods and the ‘third way’ of doing science 6. Discourse on scientific method 6.1 “The scientific method” in science education and as seen (...)
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  41. Urs Hofmann & Michael Baumgartner (2011). Determinism and the Method of Difference. Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (2):155-176.
    The first part of this paper reveals a conflict between the core principles of deterministic causation and the standard method of difference, which is widely seen (and used) as a correct method of causally analyzing deterministic structures. We show that applying the method of difference to deterministic structures can giverise to causal inferences that contradict the principles of deterministic causation. The second part then locates the source of this conflict in an inference rule implemented in the method of difference according (...)
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  42. Brendan Hogan (2008). The Imaginative Character of Pragmatic Inquiry. Cognitio Estudos 5 (2).
    John Dewey’s lifelong labor to articulate an alternative account of logic from -/- the ‘abstract thought’ predominant in discussions of logic culminates in his 1938 Logic: the -/- theory of inquiry. In this text Dewey argues that all inquiry involves the instantiation of a general -/- pattern of inquiry. Articulating the role of imagination in the general pattern of inquiry is crucial -/- to illuminating the practical character and theoretical scope of this activity. Specifically, the -/- agency of the inquirer (...)
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  43. Jeremy Howick, Against a Priori Judgements of Bad Methodology: Questioning Double-Blinding as a Universal Methodological Virtue of Clinical Trials.
    The feature of being ‘double blind’, where neither patients nor physicians are aware of who receives the experimental treatment, is universally trumpeted as being a virtue of clinical trials. The rationale for this view is unobjectionable: double blinding rules out the potential confounding influences of patient and physician beliefs. Nonetheless, viewing successfully double blind trials as necessarily superior leads to the paradox that very effective experimental treatments will not be supportable by best (double-blind) evidence. It seems strange that an account (...)
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  44. Marcus Hutter (2010). A Complete Theory of Everything (Will Be Subjective). Algorithms 3 (4):329-350.
    Increasingly encompassing models have been suggested for our world. Theories range from generally accepted to increasingly speculative to apparently bogus. The progression of theories from ego- to geo- to helio-centric models to universe and multiverse theories and beyond was accompanied by a dramatic increase in the sizes of the postulated worlds, with humans being expelled from their center to ever more remote and random locations. Rather than leading to a true theory of everything, this trend faces a turning point after (...)
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  45. Rögnvaldur Ingthorsson (2013). The Natural Vs. The Human Sciences:: Myth, Methodology and Ontology. Discusiones Filosóficas 14 (22):25-41.
    I argue that the human sciences (i.e. humanities, social- and behavioural sciences) should not try to imitate the methodology of the natural sciences. The human sciences study meaningful phenomena whose nature is decisively different from the merely physical phenomena studied by the natural sciences, and whose study therefore require different methods; meaningful phenomena do not obviously obey natural laws while the merely physical necessarily does. This is not to say that the human sciences do not study an objective reality about (...)
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  46. Michael Jacovides (2002). The Epistemology Under Locke's Corpuscularianism. Archiv für Geschichte der Philosophie 84 (2):161-189.
    The intelligibility of our artifacts suggests to many seventeenth century thinkers that nature works along analogous lines, that the same principles that explain the operations of artifacts explain the operations of natural bodies.1 We may call this belief ‘corpuscularianism’ when conjoined with the premise that the details of the analogy depend upon the sub-microscopic textures of ordinary bodies and upon the rapidly moving, imperceptibly tiny corpuscles that surround these bodies.2 Locke’s sympathy for corpuscularianism comes out clearly where he describes (...)
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  47. Marek Jakubiec (2014). Extrapolative Inference and Per Analogiam Reasoning in Empirical Sciences. Semina Scientiarum 13.
    The primary purpose of the paper is to present the issue of extrapolation, which is interesting from the perspective of contemporary philosophy of science. For its proper explanation, it is crucial to distinguish it from terms of similar meaning, such as analogy, induction or statistical inference. The second goal is to indicate key differences that exist between extrapolative inference and analogical reasoning. Because of this the ascertaining of the identification of these concepts would be a mistake though, of course, there (...)
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  48. N. Jones (2011). Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science * Edited by Deborah G. Mayo and Aris Spanos. Analysis 71 (2):406-408.
    When do data provide good evidence for a hypothesis, evidence that warrants an inference to the hypothesis? Standard answers either reject the legitimacy of induction or else allow warranted inference from data to hypothesis when there are suitable relationships between and among the data and hypotheses. The severity account rejects all of these, maintaining instead that the good evidence relation concerns not only relations between data and hypotheses but also the methods for obtaining the data and the sensitivity of these (...)
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  49. Nicholaos Jones & Olaf Wolkenhauer (2012). Diagrams as Locality Aids for Explanation and Model Construction in Cell Biology. Biology and Philosophy 27 (5):705-721.
    Using as case studies two early diagrams that represent mechanisms of the cell division cycle, we aim to extend prior philosophical analyses of the roles of diagrams in scientific reasoning, and specifically their role in biological reasoning. The diagrams we discuss are, in practice, integral and indispensible elements of reasoning from experimental data about the cell division cycle to mathematical models of the cycle’s molecular mechanisms. In accordance with prior analyses, the diagrams provide functional explanations of the cell cycle and (...)
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  50. Pawel Kawalec (2012). Moderately Pluralistic Methodology. Roczniki Filozoficzne 60 (4):233-247.
    The paper outlines and discusses the major tenets of moderately pluralistic methodology. The latter is juxtaposed to J. Życiński’s principle of natural interdisciplinarity. It instantiates scientific pluralism as a domain-specific agenda for research. The symbolic and causal understanding are integrated in this methodological conception by means of a specific kind of counterfactual reasoning, which is coined the delimiting counterfactual. It makes the moderately pluralistic methodology applicable to non-experimental research. -/- Streszczenie Tytuł: “Umiarkowanie pluralistyczna metodologia” -/- Artykuł prezentuje i omawia zasadnicze (...)
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