Search results for 'Computer Science' (try it on Scholar)

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  1. Justin Solomon (2009). Programmers, Professors, and Parasites: Credit and Co-Authorship in Computer Science. Science and Engineering Ethics 15 (4):467-489.score: 93.0
    This article presents an in-depth analysis of past and present publishing practices in academic computer science to suggest the establishment of a more consistent publishing standard. Historical precedent for academic publishing in computer science is established through the study of anecdotes as well as statistics collected from databases of published computer science papers. After examining these facts alongside information about analogous publishing situations and standards in other scientific fields, the article concludes with a list (...)
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  2. Michael J. Quinn (2006). On Teaching Computer Ethics Within a Computer Science Department. Science and Engineering Ethics 12 (2):335-343.score: 93.0
    The author has surveyed a quarter of the accredited undergraduate computer science programs in the United States. More than half of these programs offer a “social and ethical implications of computing” course taught by a computer science faculty member, and there appears to be a trend toward teaching ethics classes within computer science departments. Although the decision to create an “in house” computer ethics course may sometimes be a pragmatic response to pressure from (...)
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  3. Michael E. Cuffaro (forthcoming). How-Possibly Explanations in Quantum Computer Science. Philosophy of Science.score: 93.0
    A primary goal of quantum computer science is to find an explanation for the fact that quantum computers are more powerful than classical computers. In this paper I argue that to answer this question is to compare algorithmic processes of various kinds, and in so doing to describe the possibility spaces associated with these processes. By doing this we explain how it is possible for one process to outperform its rival. Further, in this and similar examples little is (...)
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  4. Chuck Huff, Ronald E. Anderson, Joyce Currie Little, Deborah Johnson, Rob Kling, C. Dianne Martin & Keith Miller (1996). Integrating the Ethical and Social Context of Computing Into the Computer Science Curriculum. Science and Engineering Ethics 2 (2):211-224.score: 93.0
    This paper describes the major components of ImpactCS, a program to develop strategies and curriculum materials for integrating social and ethical considerations into the computer science curriculum. It presents, in particular, the content recommendations of a subcommittee of ImpactCS; and it illustrates the interdisciplinary nature of the field, drawing upon concepts from computer science, sociology, philosophy, psychology, history and economics.
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  5. Darren Abramson (2011). Philosophy of Mind Is (in Part) Philosophy of Computer Science. Minds and Machines 21 (2):203-219.score: 90.0
    In this paper I argue that whether or not a computer can be built that passes the Turing test is a central question in the philosophy of mind. Then I show that the possibility of building such a computer depends on open questions in the philosophy of computer science: the physical Church-Turing thesis and the extended Church-Turing thesis. I use the link between the issues identified in philosophy of mind and philosophy of computer science (...)
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  6. Amnon Eden (2011). Some Philosophical Issues in Computer Science. Minds and Machines 21 (2):123-133.score: 90.0
    The essays included in the special issue dedicated to the philosophy of computer science examine new philosophical questions that arise from reflection upon conceptual issues in computer science and the insights such an enquiry provides into ongoing philosophical debates.
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  7. Timothy Colburn & Gary Shute (2007). Abstraction in Computer Science. Minds and Machines 17 (2):169-184.score: 90.0
    We characterize abstraction in computer science by first comparing the fundamental nature of computer science with that of its cousin mathematics. We consider their primary products, use of formalism, and abstraction objectives, and find that the two disciplines are sharply distinguished. Mathematics, being primarily concerned with developing inference structures, has information neglect as its abstraction objective. Computer science, being primarily concerned with developing interaction patterns, has information hiding as its abstraction objective. We show that (...)
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  8. Timothy R. Colburn (1991). Program Verification, Defeasible Reasoning, and Two Views of Computer Science. Minds and Machines 1 (1):97-116.score: 90.0
    In this paper I attempt to cast the current program verification debate within a more general perspective on the methodologies and goals of computer science. I show, first, how any method involved in demonstrating the correctness of a physically executing computer program, whether by testing or formal verification, involves reasoning that is defeasible in nature. Then, through a delineation of the senses in which programs can be run as tests, I show that the activities of testing and (...)
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  9. Timothy Colburn & Gary Shute (2011). Decoupling as a Fundamental Value of Computer Science. Minds and Machines 21 (2):241-259.score: 90.0
    Computer science is an engineering science whose objective is to determine how to best control interactions among computational objects. We argue that it is a fundamental computer science value to design computational objects so that the dependencies required by their interactions do not result in couplings, since coupling inhibits change. The nature of knowledge in any science is revealed by how concepts in that science change through paradigm shifts, so we analyze classic paradigm (...)
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  10. Timothy Colburn & Gary Shute (2010). Abstraction, Law, and Freedom in Computer Science. Metaphilosophy 41 (3):345-364.score: 90.0
    Abstract: Laws of computer science are prescriptive in nature but can have descriptive analogs in the physical sciences. Here, we describe a law of conservation of information in network programming, and various laws of computational motion (invariants) for programming in general, along with their pedagogical utility. Invariants specify constraints on objects in abstract computational worlds, so we describe language and data abstraction employed by software developers and compare them to Floridi's concept of levels of abstraction. We also consider (...)
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  11. Amnon H. Eden (2007). Three Paradigms of Computer Science. Minds and Machines 17 (2):135-167.score: 90.0
    We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within (...)
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  12. Thomas Ehrhard (ed.) (2004). Linear Logic in Computer Science. Cambridge University Press.score: 90.0
    Linear Logic is a branch of proof theory which provides refined tools for the study of the computational aspects of proofs. These tools include a duality-based categorical semantics, an intrinsic graphical representation of proofs, the introduction of well-behaved non-commutative logical connectives, and the concepts of polarity and focalisation. These various aspects are illustrated here through introductory tutorials as well as more specialised contributions, with a particular emphasis on applications to computer science: denotational semantics, lambda-calculus, logic programming and concurrency (...)
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  13. William J. Rapaport (2005). Philosophy of Computer Science. Teaching Philosophy 28 (4):319-341.score: 90.0
    There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.
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  14. Raymond Turner, The Philosophy of Computer Science. Stanford Encyclopedia of Philosophy.score: 75.0
  15. Oron Shagrir (1999). What is Computer Science About? The Monist 82 (1):131-149.score: 75.0
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  16. Roy Dowsing (1986). A First Course in Formal Logic and its Applications in Computer Science. Blackwell Scientific Publications.score: 75.0
  17. Jordi Vallverdú (ed.) (2010). Thinking Machines and the Philosophy of Computer Science: Concepts and Principles. Information Science Reference.score: 75.0
  18. Roman Murawski (1997). Gödel's Incompleteness Theorems and Computer Science. Foundations of Science 2 (1):123-135.score: 63.0
    In the paper some applications of Gödel's incompleteness theorems to discussions of problems of computer science are presented. In particular the problem of relations between the mind and machine (arguments by J.J.C. Smart and J.R. Lucas) is discussed. Next Gödel's opinion on this issue is studied. Finally some interpretations of Gödel's incompleteness theorems from the point of view of the information theory are presented.
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  19. Matti Tedre (2011). Computing as a Science: A Survey of Competing Viewpoints. [REVIEW] Minds and Machines 21 (3):361-387.score: 63.0
    Since the birth of computing as an academic discipline, the disciplinary identity of computing has been debated fiercely. The most heated question has concerned the scientific status of computing. Some consider computing to be a natural science and some consider it to be an experimental science. Others argue that computing is bad science, whereas some say that computing is not a science at all. This survey article presents viewpoints for and against computing as a science. (...)
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  20. Charles Glagola, Moshe Kam, Caroline Whitebeck & Michael C. Loui (1997). Teaching Ethics in Engineering and Computer Science: A Panel Discussion. Science and Engineering Ethics 3 (4):463-480.score: 63.0
    At a conference, two engineering professors and a philosophy professor discussed the teaching of ethics in engineering and computer science. The panelists considered the integration of material on ethics into technical courses, the role of ethical theory in teaching applied ethics, the relationship between cases and codes of ethics, the enlisting of support of engineering faculty, the background needed to teach ethics, and the assessment of student outcomes. Several audience members contributed comments, particularly on teaching ethical theory and (...)
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  21. Jordi Fernández (2003). Explanation by Computer Simulation in Cognitive Science. Minds And Machines 13 (2):269-284.score: 60.0
    My purpose in this essay is to clarify the notion of explanation by computer simulation in artificial intelligence and cognitive science. My contention is that computer simulation may be understood as providing two different kinds of explanation, which makes the notion of explanation by computer simulation ambiguous. In order to show this, I shall draw a distinction between two possible ways of understanding the notion of simulation, depending on how one views the relation in which a (...)
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  22. G. Crocco, Luis Fariñas del Cerro & Andreas Herzig (eds.) (1995). Conditionals: From Philosophy to Computer Science. Oxford University Press.score: 60.0
    This book looks at the ways in which conditionals, an integral part of philosophy and logic, can be of practical use in computer programming. It analyzes the different types of conditionals, including their applications and potential problems. Other topics include defeasible logics, the Ramsey test, and a unified view of consequence relation and belief revision. Its implications will be of interest to researchers in logic, philosophy, and computer science, particularly artificial intelligence.
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  23. Fred Boogerd, Frank Bruggeman, Catholijn Jonker, Huib Looren de Jong, Allard Tamminga, Jan Treur, Hans Westerhoff & Wouter Wijngaards (2002). Inter-Level Relations in Computer Science, Biology, and Psychology. Philosophical Psychology 15 (4):463–471.score: 60.0
    Investigations into inter-level relations in computer science, biology and psychology call for an *empirical* turn in the philosophy of mind. Rather than concentrate on *a priori* discussions of inter-level relations between 'completed' sciences, a case is made for the actual study of the way inter-level relations grow out of the developing sciences. Thus, philosophical inquiries will be made more relevant to the sciences, and, more importantly, philosophical accounts of inter-level relations will be testable by confronting them with what (...)
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  24. Gregory Linshiz, Alex Goldberg, Tania Konry & Nathan J. Hillson (2013). The Fusion of Biology, Computer Science, and Engineering: Towards Efficient and Successful Synthetic Biology. Perspectives in Biology and Medicine 55 (4):503-520.score: 60.0
    The integration of computer science, biology, and engineering has resulted in the emergence of rapidly growing interdisciplinary fields such as bioinformatics, bioengineering, DNA computing, and systems and synthetic biology. Ideas derived from computer science and engineering can provide innovative solutions to biological problems and advance research in new directions. Although interdisciplinary research has become increasingly prevalent in recent years, the scientists contributing to these efforts largely remain specialists in their original disciplines and are not fully capable (...)
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  25. Ron Loui, Review of Deontic Logic in Computer Science. [REVIEW]score: 60.0
    Most of the papers in this collection are from the First International Workshop on Deontic Logic in Computer Science, DEON91, held in Amsterdam in December 1991. AI (especially AI and law, and knowledge representation) and formal system specification are the computer science communities that would seem to be most interested. In fact, this reviewer, a researcher in AI, was surprised to find common ground with a visiting researcher in distributed systems by discussing the contents of this (...)
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  26. M. Ben-Ari (1993/2003). Mathematical Logic for Computer Science. Prentice Hall.score: 60.0
    Mathematical Logic for Computer Science is a mathematics textbook with theorems and proofs, but the choice of topics has been guided by the needs of computer science students. The method of semantic tableaux provides an elegant way to teach logic that is both theoretically sound and yet sufficiently elementary for undergraduates. To provide a balanced treatment of logic, tableaux are related to deductive proof systems.The logical systems presented are:- Propositional calculus (including binary decision diagrams);- Predicate calculus;- (...)
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  27. Douglas Walton (2000). The Place of Dialogue Theory in Logic, Computer Science and Communication Studies. Synthese 123 (3):327-346.score: 60.0
    Dialogue theory, although it has ancient roots, was put forward in the 1970s in logic as astructure that can be useful for helping to evaluate argumentation and informal fallacies.Recently, however, it has been taken up as a broader subject of investigation in computerscience. This paper surveys both the historical and philosophical background of dialoguetheory and the latest research initiatives on dialogue theory in computer science. The main components of dialogue theory are briefly explained. Included is a classification of (...)
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  28. James H. Fetzer (1999). The Role Of Models In Computer Science. The Monist 82 (1):20-36.score: 60.0
    Taking Brian Cantwell Smith’s study, “Limits of Correctness in Computers,” as its point of departure, this article explores the role of models in computer science. Smith identifies two kinds of models that play an important role, where specifications are models of problems and programs are models of possible solutions. Both presuppose the existence of conceptualizations as ways of conceiving the world “in certain delimited ways.” But high-level programming languages also function as models of virtual (or abstract) machines, while (...)
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  29. Daniel Saunders & Paul Thagard, Creativity in Computer Science.score: 60.0
    Computer science only became established as a field in the 1950s, growing out of theoretical and practical research begun in the previous two decades. The field has exhibited immense creativity, ranging from innovative hardware such as the early mainframes to software breakthroughs such as programming languages and the Internet. Martin Gardner worried that "it would be a sad day if human beings, adjusting to the Computer Revolution, became so intellectually lazy that they lost their power of creative (...)
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  30. Uta Priss (2008). Facet-Like Structures in Computer Science. Axiomathes 18 (2):243-255.score: 60.0
    This paper discusses how facet-like structures occur as a commonplace feature in a variety of computer science disciplines as a means for structuring class hierarchies. The paper then focuses on a mathematical model for facets (and class hierarchies in general), called formal concept analysis, and discusses graphical representations of faceted systems based on this model.
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  31. Marvin Minsky, Form and Content in Computer Science.score: 60.0
    An excessive preoccupation with formalism is impeding the development of computer science. Form-content confusion is discussed relative to three areas: theory of computation, programming languages, and education.
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  32. Dag Normann (2006). Computing with Functionals: Computability Theory or Computer Science? Bulletin of Symbolic Logic 12 (1):43-59.score: 60.0
    We review some of the history of the computability theory of functionals of higher types, and we will demonstrate how contributions from logic and theoretical computer science have shaped this still active subject.
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  33. William J. Rapaport (2005). Philosophy of Computer Science : An Introductory Course Philosophy of Computer Science : An Introductory Course. Teaching Philosophy 28 (4):319-341.score: 60.0
    There are many branches of philosophy called "the philosophy of X," where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.
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  34. Robert L. Constable, The Triumph of Types: Principia Mathematica's Impact on Computer Science.score: 60.0
    Types now play an essential role in computer science; their ascent originates from Principia Mathematica. Type checking and type inference algorithms are used to prevent semantic errors in programs, and type theories are the native language of several major interactive theorem provers. Some of these trace key features back to Principia.
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  35. Enrico Franconi, Computer Science & IT with/for Biology.score: 60.0
    This reader contains the extended abstracts of the seminars organised for the “Computer Science and IT with/for Biology” Seminar Series, held at the Faculty of Computer Science, Free University of Bozen-Bolzano, from October to December 2005. Slides of the presentations are available online at: www.inf.unibz.it/krdb/biology.
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  36. Peter Wegner (1999). Towards Empirical Computer Science. The Monist 82 (1):58-108.score: 60.0
    Part I presents a model of interactive computation and a metric for expressiveness, Part II relates interactive models of computation to physics, and Part III considers empirical models from a philosophical perspective. Interaction machines, which extend Turing Machines to interaction, are shown in Part I to be more expressive than Turing Machines by a direct proof, by adapting Gödel's incompleteness result, and by observability metrics. Observation equivalence provides a tool for measuring expressiveness according to which interactive systems are more expressive (...)
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  37. Eric B. Winsberg (2010). Science in the Age of Computer Simulation. The University of Chicago Press.score: 60.0
    Introduction -- Sanctioning models : theories and their scope -- Methodology for a virtual world -- A tale of two methods -- When theories shake hands -- Models of climate : values and uncertainties -- Reliability without truth -- Conclusion.
     
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  38. Aaron Sloman (1978). The Computer Revolution in Philosophy: Philosophy Science and Models of Mind. Harvester.score: 57.0
    Since 1991 the author has been Professor of Artificial Intelligence and Cognitive Science in the School of Computer Science at the University of Birmingham, UK.
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  39. Hidde de Jong, Nicolaas Mars & Paul van der Vet (1999). Computer-Supported Resolution of Measurement Conflicts: A Case-Study in Materials Science. [REVIEW] Foundations of Science 4 (4):427-461.score: 57.0
    Resolving conflicts between different measurements ofa property of a physical system may be a key step in a discoveryprocess. With the emergence of large-scale databases and knowledgebases with property measurements, computer support for the task ofconflict resolution has become highly desirable. We will describe amethod for model-based conflict resolution and the accompanyingcomputer tool KIMA, which have been applied in a case-study inmaterials science. In order to be a useful aid to scientists, the toolneeds to be integrated with other (...)
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  40. Florence Appel (2005). Ethics Across the Computer Science Curriculum: Privacy Modules in an Introductory Database Course. Science and Engineering Ethics 11 (4):635-644.score: 57.0
    This paper describes the author’s experience of infusing an introductory database course with privacy content, and the on-going project entitled Integrating Ethics Into the Database Curriculum, that evolved from that experience. The project, which has received funding from the National Science Foundation, involves the creation of a set of privacy modules that can be implemented systematically by database educators throughout the database design thread of an undergraduate course.
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  41. Margaret A. Boden (1988). Computer Models On Mind: Computational Approaches In Theoretical Psychology. Cambridge University Press.score: 54.0
    What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena--including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For (...)
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  42. Douglas S. Robertson (2003). Phase Change: The Computer Revolution in Science and Mathematics. Oxford University Press.score: 54.0
    Robertson's earlier work, The New Renaissance projected the likely future impact of computers in changing our culture. Phase Change builds on and deepens his assessment of the role of the computer as a tool driving profound change by examining the role of computers in changing the face of the sciences and mathematics. He shows that paradigm shifts in understanding in science have generally been triggered by the availability of new tools, allowing the investigator a new way of seeing (...)
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  43. A. P. Ershov & Donald Ervin Knuth (eds.) (1981). Algorithms in Modern Mathematics and Computer Science: Proceedings, Urgench, Uzbek Ssr, September 16-22, 1979. Springer-Verlag.score: 51.0
  44. William J. McKinney (1997). The Educational Use of Computer Based Science Simulations: Some Lessons From the Philosophy of Science. Science and Education 6 (6):591-603.score: 51.0
    Examines some of the potential and some of the problems inherent in using computerized simulations in science and science studies classes by applying lessons from the epistemology of science. While computer simulations are useful pedagogical tools, they are not experiments and thus are of only limited utility as substitutes for actual laboratories. Contains 20 references. (Author/PVD).
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  45. Peter Kugel (1996). Implicit Learning From a Computer-Science Perspective. Behavioral and Brain Sciences 19 (3):556-557.score: 49.0
    Shanks and St. John (1994a) suggest that From the viewpoint of a computer scientist who tries to construct learning systems, that claim seems rather implausible. In this commentary I wish to suggest why, in the hopes of shedding light on the relationship between consciousness and learning.
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  46. Eric Winsberg (2009). Computer Simulation and the Philosophy of Science. Philosophy Compass 4 (5):835-845.score: 48.0
    There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and (...)
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  47. Paul Humphreys (2009). The Philosophical Novelty of Computer Simulation Methods. Synthese 169 (3):615 - 626.score: 48.0
    Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.
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  48. Henk Barendregt (1997). The Impact of the Lambda Calculus in Logic and Computer Science. Bulletin of Symbolic Logic 3 (2):181-215.score: 48.0
    One of the most important contributions of A. Church to logic is his invention of the lambda calculus. We present the genesis of this theory and its two major areas of application: the representation of computations and the resulting functional programming languages on the one hand and the representation of reasoning and the resulting systems of computer mathematics on the other hand.
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  49. James H. Moor (1978). Three Myths of Computer Science. British Journal for the Philosophy of Science 29 (3):213-222.score: 48.0
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  50. Wendy Parker (2012). Computer Simulation and Philosophy of Science. Metascience 21 (1):111-114.score: 48.0
    Computer simulation and philosophy of science Content Type Journal Article Pages 1-4 DOI 10.1007/s11016-011-9567-8 Authors Wendy S. Parker, Department of Philosophy, Ellis Hall 202, Ohio University, Athens, OH 45701, USA Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.
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