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  1. Inductive Inference and Unsolvability.Leonard M. Adleman & M. Blum - 1991 - Journal of Symbolic Logic 56 (3):891-900.
    It is shown that many different problems have the same degree of unsolvability. Among these problems are: THE INDUCTIVE INFERENCE PROBLEM. Infer in the limit an index for a recursive function f presented as f(0), f(1), f(2),.... THE RECURSIVE INDEX PROBLEM. Decide in the limit if i is the index of a total recursive function. THE ZERO NONVARIANT PROBLEM. Decide in the limit if a recursive function f presented as f(0), f(1), f(2),... has value unequal to zero for infinitely many (...)
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  2. Falsification and Future Performance.David Balduzzi - manuscript -
    We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsifies when it finds the classifier in its repertoire minimizing empirical risk. It then follows from that the future performance of predictors on unseen data is controlled in part by how many hypotheses the learner falsifies. As a corollary we show that empirical VC-entropy quantifies the message (...)
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  3. Falsifiable Implies Learnable.David Balduzzi - manuscript -
    The paper demonstrates that falsifiability is fundamental to learning. We prove the following theorem for statistical learning and sequential prediction: If a theory is falsifiable then it is learnable -- i.e. admits a strategy that predicts optimally. An analogous result is shown for universal induction.
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  4. Information, Learning and Falsification.David Balduzzi - manuscript -
    There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The third, statistical learning (...)
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  5. Watkins and the Pragmatic Problem of Induction.Greg Bamford - 1989 - Analysis 49 (4):203 - 205..
    Watkins proposes a neo-Popperian solution to the pragmatic problem of induction. He asserts that evidence can be used non-Inductively to prefer the principle that corroboration is more successful over all human history than that, Say, Counter-Corroboration is more successful either over this same period or in the future. Watkins's argument for rejecting the first counter-Corroborationist alternative is beside the point, However, As whatever is the best strategy over all human history is irrelevant to the pragmatic problem of induction since we (...)
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  6. Sober as a Judge.Gordon Belot - 2016 - Metascience 25 (3):387-392.
    In Ockham's Razors: A User's Guide, Elliott Sober argues that parsimony considerations are epistemically relevant on the grounds that certain methods of model selection, such as the Akaike Information Criterion, exhibit good asymptotic behaviour and take the number of adjustable parameters in a model into account. I raise some worries about this form of argument.
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  7. Ampliative Abduction.James Blachowicz - 1996 - International Studies in the Philosophy of Science 10 (2):141 – 157.
    Abstract In Peirce's and Hanson's characterization of abductive inference, the abducted hypothesis (but not others) is present in the premises, so that the inference can hardly be taken as ampliative. Abduction has consequently been treated as part of the process whereby already generated hypotheses are judged in terms of their plausibility, simplicity, etc. I propose an interpretation of abduction which supports an ampliative view. It relies on a distinction between two logical stages in the generation of hypotheses, one ?factual? and (...)
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  8. Reason and Prediction.Simon Blackburn - 1973 - London: Cambridge University Press.
    An original study of the philosophical problems associated with inductive reasoning. Like most of the main questions in epistemology, the classical problem of induction arises from doubts about a mode of inference used to justify some of our most familiar and pervasive beliefs. The experience of each individual is limited and fragmentary, yet the scope of our beliefs is much wider; and it is the relation between belief and experience, in particular the belief that the future will in some respects (...)
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  9. Assessing Theories: The Coherentist Approach.Peter Brössel - 2013 - 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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  10. The Domain Constraint on Analogy and Analogical Argument.William R. Brown - 1995 - Informal Logic 17 (1).
    Domain constraint, the requirement that analogues be selected from "the same category," inheres in the popular saying "you can't compare apples and oranges" and the textbook principle "the greater the number of shared properties, the stronger the argument from analogy." I identify roles of domains in biological, linguistic, and legal analogy, supporting the account of law with a computer word search of judicial decisions. I argue that the category treatments within these disciplines cannot be exported to general informal logic, where (...)
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  11. The Semimeasure Property of Algorithmic Probability -- “Feature‘ or “Bug‘?Douglas Campbell - 2013 - In David L. Dowe (ed.), Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers From the Ray Solomonoff 85th Memorial Conference, Melbourne, Vic, Australia, November 30 -- December 2, 2011. Springer Berlin Heidelberg. pp. 79--90.
    An unknown process is generating a sequence of symbols, drawn from an alphabet, A. Given an initial segment of the sequence, how can one predict the next symbol? Ray Solomonoff’s theory of inductive reasoning rests on the idea that a useful estimate of a sequence’s true probability of being outputted by the unknown process is provided by its algorithmic probability (its probability of being outputted by a species of probabilistic Turing machine). However algorithmic probability is a “semimeasure”: i.e., the sum, (...)
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  12. Discovering Relativity Beliefs: Towards a Socio-Cognitive Model for Einstein's Relativity Theory Formation.Andrea Cerroni - 2002 - Mind and Society 3 (1):93-109.
    The research on which the present paper makes a point in aimed at designing a cognitive model of Albert Einstein's discovery that is based on fundamental Einstein's publications and placed, ideally, at a meso-level, between macro-historical and micro-cognitive reconstructions (e.g. protocol analysis). As in a cognitive-historical analysis, we will trace some discovery heuristics in the construction of representations, that are on a continuum with those we employ in ordinary problem solving. Firstly, some theory-specific, reflexive heuristics—named orientative heuristics—are traced: inner perfection, (...)
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  13. Covariance/Invariance: A Cognitive Heuristic in Einstein's Relativity Theory Formation. [REVIEW]Andrea Cerroni - 2000 - Foundations of Science 5 (2):209-224.
    Relativity Theory by Albert Einstein has been so far littleconsidered by cognitive scientists, notwithstanding its undisputedscientific and philosophical moment. Unfortunately, we don't have adiary or notebook as cognitively useful as Faraday's. But physicshistorians and philosophers have done a great job that is relevant bothfor the study of the scientist's reasoning and the philosophy ofscience. I will try here to highlight the fertility of a `triangulation'using cognitive psychology, history of science and philosophy of sciencein starting answering a clearly very complex question:why (...)
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  14. Foundations of Inductive Reasoning.Ronald Christensen - 1964 - Entropy, Ltd.].
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  15. The Role of Inductive Reasoning in the Interpretation of Metaphor.L. Jonathan Cohen & Avishai Margalit - 1970 - Synthese 21 (3-4):469 - 487.
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  16. Analogy.Todd Davies - manuscript -
    This essay (my undergraduate honors thesis at Stanford, issued by the Center for the Study of Language and Information in November 1985) constructs a theory of analogy as it applies to argumentation and reasoning, especially as used in fields such as philosophy and law. The word analogy has been used in different senses, which the essay defines. The theory developed herein applies to analogia rationis, or analogical reasoning. Building on the framework of situation theory, a type of logical relation called (...)
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  17. A Logical Approach to Reasoning by Analogy.Todd R. Davies & Stuart J. Russell - 1987 - In John P. McDermott (ed.), Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI'87). Morgan Kaufmann Publishers. pp. 264-270.
    We analyze the logical form of the domain knowledge that grounds analogical inferences and generalizations from a single instance. The form of the assumptions which justify analogies is given schematically as the "determination rule", so called because it expresses the relation of one set of variables determining the values of another set. The determination relation is a logical generalization of the different types of dependency relations defined in database theory. Specifically, we define determination as a relation between schemata of first (...)
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  18. Necessary Connexion and Inductive Reasoning.W. Geo Davies - 1878 - Mind 3 (11):417-424.
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  19. A Classic of Bayesian Confirmation Theory. [REVIEW]Finnur Dellsén - forthcoming - Metascience.
    Book review of Paul Horwich, Probability and Evidence (Cambridge Philosophy Classics edition), Cambridge: Cambridge University Press, 2016, 147pp, £14.99 (paperback).
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  20. Reactionary Responses to the Bad Lot Objection.Finnur Dellsén - 2017 - Studies in History and Philosophy of Science Part A 61:32-40.
    As it is standardly conceived, Inference to the Best Explanation (IBE) is a form of ampliative inference in which one infers a hypothesis because it provides a better potential explanation of one’s evidence than any other available, competing explanatory hypothesis. Bas van Fraassen famously objected to IBE thus formulated that we may have no reason to think that any of the available, competing explanatory hypotheses are true. While revisionary responses to the Bad Lot Objection concede that IBE needs to be (...)
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  21. Inductive Reasoning and the Uniformity of Nature.Aron Edidin - 1984 - Journal of Philosophical Logic 13 (3):285 - 302.
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  22. Observation and Induction.Theodore J. Everett - 2010 - Logos and Episteme 1 (2):303-324.
    This article offers a simple technical resolution to the problem of induction, which is to say that general facts are not always inferred from observations of particular facts, but are themselves sometimes defeasibly observed. The article suggests a holistic account of observation that allows for general statements in empirical theories to be interpreted as observation reports, in place of the common but arguably obsolete idea that observations are exclusively particular. Predictions and other particular statements about unobservable facts can then appear (...)
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  23. Inductive Reasoning and Semantic Cognition: More Than Just Different Names for the Same Thing?Aidan Feeney, Aimee K. Crisp & Catherine J. Wilburn - 2008 - Behavioral and Brain Sciences 31 (6):715-716.
    We describe evidence that certain inductive phenomena are associated with IQ, that different inductive phenomena emerge at different ages, and that the effects of causal knowledge on induction are decreased under conditions of memory load. On the basis of this evidence we argue that there is more to inductive reasoning than semantic cognition.
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  24. Properties of the Diversity Effect in Category-Based Inductive Reasoning.Aidan Feeney & Evan Heit - 2011 - Thinking and Reasoning 17 (2):156 - 181.
    Four experiments investigated how people judge the plausibility of category-based arguments, focusing on the diversity effect, in which arguments with diverse premise categories are considered particularly strong. In Experiment 1 we show that priming people as to the nature of the blank property determines whether sensitivity to diversity is observed. In Experiment 2 we find that people's hypotheses about the nature of the blank property predict judgements of argument strength. In Experiment 3 we examine the effect of our priming methodology (...)
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  25. Therapeutic Inferences for Individual Patients.Luis J. Flores - 2015 - Journal of Evaluation in Clinical Practice 21 (3):440-447.
    RATIONALE, AIMS AND OBJECTIVES: Increased awareness of the gap between controlled research and medical practice has raised concerns over whether the special attention of doctors to probability estimates from clinical trials really improves the care of individuals. Evidence-based medicine has acknowledged that research results are not applicable to all kinds of patients, and consequently, has attempted to overcome this limitation by introducing improvements in the design and analysis of clinical trials. METHODS: A clinical case is used to highlight the premises (...)
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  26. Necessary Connexion and Inductive Reasoning.W. GeoDavies - 1878 - Mind 3 (11):417-424.
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  27. Bridging Learning Theory and Dynamic Epistemic Logic.Nina Gierasimczuk - 2009 - Synthese 169 (2):371-384.
    This paper discusses the possibility of modelling inductive inference (Gold 1967) in dynamic epistemic logic (see e.g. van Ditmarsch et al. 2007). The general purpose is to propose a semantic basis for designing a modal logic for learning in the limit. First, we analyze a variety of epistemological notions involved in identification in the limit and match it with traditional epistemic and doxastic logic approaches. Then, we provide a comparison of learning by erasing (Lange et al. 1996) and iterated epistemic (...)
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  28. The Testing Principle: Inductive Reasoning and the Ellsberg Paradox.Gary Gigliotti - 1996 - Thinking and Reasoning 2 (1):33 – 49.
    We postulate the Testing Principle : that individuals ''act like statisticians'' when they face uncertainty in a decision problem, ranking alternatives to the extent that available evidence allows. The Testing Principle implies that completeness of preferences, rather than the sure-thing principle , is violated in the Ellsberg Paradox. In the experiment, subjects chose between risky and uncertain acts in modified Ellsberg-type urn problems, with sample information about the uncertain urn. Our results show, consistent with the Testing Principle, that the uncertain (...)
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  29. Is the Model Theory of Induction Also a Theory of Inductive Reasoning?Vittorio Girotto - 1994 - International Studies in the Philosophy of Science 8 (1):41 – 43.
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  30. Deductivism as an Interpretative Strategy: A Reply to Groarke's Defense of Reconstructive Deductivism.David Godden - 2005 - Argumentation and Advocacy: Journal of the American Forensic Association 41:168-183.
    Deductivism has been variously presented as an evaluative thesis and as an interpretive one. I argue that deductivism fails as a universal evaluative thesis, and as such that its value as an interpretive thesis must be supported on other grounds. As a reconstructive strategy, deductivism is justified only on the grounds that an arguer is, or ought to be, aiming at the deductive standard of evidence. As such, the reconstruction of an argument as deductive must be supported by contextual and (...)
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  31. Hume's “Circularity” Charge Against Inductive Reasoning.D. Goldstick - 1972 - Dialogue 11 (2):258-266.
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  32. Nondeductive Inference. [REVIEW]P. K. H. - 1968 - Review of Metaphysics 21 (3):546-546.
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  33. Induction and Probability.Ned Hall & Alan Hájek - 2002 - In Peter Machamer & Michael Silberstein (eds.), The Blackwell Guide to the Philosophy of Science. pp. 149-172.
    Arguably, Hume's greatest single contribution to contemporary philosophy of science has been the problem of induction (1739). Before attempting its statement, we need to spend a few words identifying the subject matter of this corner of epistemology. At a first pass, induction concerns ampliative inferences drawn on the basis of evidence (presumably, evidence acquired more or less directly from experience)—that is, inferences whose conclusions are not (validly) entailed by the premises. Philosophers have historically drawn further distinctions, often appropriating the term (...)
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  34. Statistical Learning Theory as a Framework for the Philosophy of Induction.Gilbert Harman & Sanjeev Kulkarni - manuscript -
    Statistical Learning Theory (e.g., Hastie et al., 2001; Vapnik, 1998, 2000, 2006) is the basic theory behind contemporary machine learning and data-mining. We suggest that the theory provides an excellent framework for philosophical thinking about inductive inference.
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  35. Discussion: Re‐Solving Irrelevant Conjunction with Probabilistic Independence.James Hawthorne & Branden Fitelson - 2004 - Philosophy of Science 71 (4):505-514.
    Naive deductivist accounts of confirmation have the undesirable consequence that if E confirms H, then E also confirms the conjunction H·X, for any X—even if X is completely irrelevant to E and H. Bayesian accounts of confirmation may appear to have the same problem. In a recent article in this journal Fitelson (2002) argued that existing Bayesian attempts to resolve of this problem are inadequate in several important respects. Fitelson then proposes a new‐and‐improved Bayesian account that overcomes the problem of (...)
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  36. Causal Relations and Feature Similarity in Children's Inductive Reasoning.Brett K. Hayes & Susan P. Thompson - 2007 - Journal of Experimental Psychology 136:470-485.
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  37. Properties of Inductive Reasoning.Evan Heit - 2000 - Psychonomic Bulletin and Review 7:569-592.
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  38. Similarity and Property Effects in Inductive Reasoning.Evan Heit & Joshua Rubinstein - 1994 - Journal of Experimental Psychology 20:411-422.
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  39. The Bayes Rule is Not Sufficient to Justify or Describe Inductive Reasoning.Jürgen Humburg - 1987 - Erkenntnis 26 (3):379 - 390.
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  40. The Collapse of Collective Defeat: Lessons From the Lottery Paradox.Kevin B. Korb - 1992 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:230-236.
    The Lottery Paradox has been thought to provide a reductio argument against probabilistic accounts of inductive inference. As a result, much work in artificial intelligence has concentrated on qualitative methods of inference, including default logics, which are intended to model some varieties of inductive inference. It has recently been shown that the paradox can be generated within qualitative default logics. However, John Pollock's qualitative system of defeasible inference, does avoid the Lottery Paradox by incorporating a rule designed specifically for that (...)
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  41. Inferring Category Charachteristics From Sample Charachteristics: Inductive Reasoning and Social Projection.Joachim Krueger & Russel W. Clement - 1996 - Journal of Experimental Psychology 125 (1):52-68.
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  42. Endogenous Constraints on Inductive Reasoning.Andre Kukla - 1992 - Philosophical Psychology 5 (4):411 – 425.
    It is widely recognized that computational theories of learning must posit the existence of a priori constraints on hypothesis selection. The present article surveys the theoretical options available for modelling the dynamic process whereby the constraints have their effect. According to the 'simplicity' theory (exemplified by Fodor's treatment), hypotheses are preference-ordered in terms of their syntactic or semantic properties. It is argued that the same explanatory power can be obtained with a weaker (hence better) theory, the 'minimalist' theory, which dispenses (...)
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  43. An Abstract Model for Inductive Inference.Bernhard Lauth - 1994 - Erkenntnis 40 (1):87 - 120.
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  44. Support and Surprise: L. J. Cohen's View of Inductive Probability. [REVIEW]Isaac Levi - 1979 - British Journal for the Philosophy of Science 30 (3):279-292.
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  45. The Problem of Piecemeal Induction.Conor Mayo-Wilson - 2011 - Philosophy of Science 78 (5):864-874.
    It is common to assume that the problem of induction arises only because of small sample sizes or unreliable data. In this paper, I argue that the piecemeal collection of data can also lead to underdetermination of theories by evidence, even if arbitrarily large amounts of completely reliable experimental and observational data are collected. Specifically, I focus on the construction of causal theories from the results of many studies (perhaps hundreds), including randomized controlled trials and observational studies, where the studies (...)
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  46. Why Simpler Arguments Are Better.Moti Mizrahi - 2016 - Argumentation 30 (3):247-261.
    In this paper, I argue that, other things being equal, simpler arguments are better. In other words, I argue that, other things being equal, it is rational to prefer simpler arguments over less simple ones. I sketch three arguments in support of this claim: an argument from mathematical proofs, an argument from scientific theories, and an argument from the conjunction rule.
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  47. Are Inductive Generalizations Quantifiable?John O. Nelson - 1962 - Analysis 22 (3):59 - 65.
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  48. Inductive Reasoning, Deductive Reasoning and Mental Models.Stephen E. Newstead - 1994 - International Studies in the Philosophy of Science 8 (1):65 – 67.
    (1994). Inductive reasoning, deductive reasoning and mental models. International Studies in the Philosophy of Science: Vol. 8, No. 1, pp. 65-67. doi: 10.1080/02698599408573483.
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  49. The Use of Statistical Heuristics in Everyday Inductive Reasoning.E. Nisbett Richard, H. Krantz David, Jepson Christopher & Kunda Ziva - 1983 - Psychological Review 90 (4):339-363.
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  50. The Sophisticated Inductive Approach and Science Education.Khosrow Bagheri Noaparast, Zahra Niknam & Mohammad Zoheir Bagheri Noaparast - 2011 - Procedia - Social and Behavioral Sciences.
    Introduction: The aim of the present study was to explore the relationship between sophisticated view of induction and science education. Method: This study is a critical review on the relation between philosophical approaches to science and science education. Thus, an analytic method is used in investigating the theories of science and their relationship to science education. Results: Analyzing the arguments against induction, we argue that the sophisticated view of induction is not only resistant against the critiques but also inspiring for (...)
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