Philosophy of science.

By R. Persaud. 1992

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Citations in the corpus

Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences (2017, methodology): “Specifically, two well-known theories of explanation, sometimes cited and used in explainable AI articles, are the logically deductive model of explanation [Hempel and Oppenheim, 1948], and the covariation model [Kelley, 1967]; both of which have had significant impact.”
Synthesis of Discipline-Based Education Research in Physics. (2014, background): “Strike and Posner [39] have since revised their initial views somewhat to include issues related to the learner’s “conceptual ecology” as well as developmental and interactionist considerations.”
Open data policies, their implementation and impact: A framework for comparison (2014, methodology): “Following Bergmann’s (1957) approach of observation, induction and”
Ifs, Ands, and Buts: An Incremental Truthmaker Semantics for Indicative Conditionals (2016, background): “41Horgan’s counterfactual style of if-thenism runs into an analogous problem ([Horgan(1984)]).”
Competitive advantage: logical and philosophical considerations (2001, unknown): “Under pragmatism, hypotheses can be justified through the inferential method of “abduction”, sometimes called “inference to the best explanation” (O’Hear, 1989; Sklar, 1995).”
Resurrecting Logical Probability (2001, background): “…between the two situations already exists, in Keynes concept of " weight " (Keynes 1921, ch. 6; see Cohen 1986; O'Donnell 1992; Shafer 1976, pp. 5–6; Achinstein 1994; a recent attempt to quantify weight in Jaynes, to appear, ch. 18) The standard example to introduce the concept of weight…”
Discovery and Creation: Alternative Theories of Entrepreneurial Action (2007, background): “…critical assumptions: (1) assumptions about the nature of human objectives, (2) assumptions about the nature of individuals, and (3) assumptions about the nature of the decision making context within which individuals operate (Bergmann, 1957; Nagal, 1961; Parsons, 1951; Parsons and Shils, 1962).”
Evaluating and Aggregating Feature-based Model Explanations (2020, background): “Since humans cannot process a lot of information at once, some have argued for minimal model explanations that contain only relevant and representative features [Batterman and Rice, 2014]; therefore, an explanations should not be complex (i.”
Forty Years of ‘The Strategy’: Levins on Model Building and Idealization (2007, background): “(Friedman, 1974; Kitcher, 1981) These are clearly advantages of the third strategy and may be why Levins seems to favor the strategy above the others.”
The Promise and Potential of Big Data: A Case for Discovery Informatics (2014, background): “…that for the first time allowed precise descriptions of rate of change, and hence fundamental constructs of classical physics such as velocity and acceleration, that helped transform the study of the physical universe from “stamp collecting” to “physics,” from a descriptive science into a…”
Comparing the Epistemological Underpinnings of Students' and Scientists' Reasoning about Conclusions. (2001, background): “…in reasoning according to contemporary philosophy of science and cognitive perspectives on basic principles and standards for making valid inferences and arguments from evidence (Boyd et al., 1990; Cole, 1992; Giere, 1984; Schauble, 1996)Ðthe reasoning [National Research Council (NRC), 1996].”
Conceptual Resources for Learning Science: Issues of transience and grain‐size in cognition and cognitive structure (2008, background): “…data that the learner has available to him [sic] at any point in time’ (Ausubel and Robinson, 1971, p.51) would seem to be better considered as components of a learner’s conceptual ecology (Duschl & Hamilton 1992), which will be represented in some form in the individual’s cognitive structure.”
The explanation paradox (2012, background): “Finally, the unifying power of a basis (with respect to K) varies directly with: (1) the number of conclusions that can be derived from the set of arguments it generates; and (2) the stringency of its argument patterns, and it varies inversely with the number of its members (Kitcher 1981, p. 520).”
The Nature of Elementary Student Science Discourse in the Context of the Science Writing Heuristic Approach (2010, background): “Kuhn’s perspective on the nature of knowledge generation in science has taken root among science historians, philosophers, educators, and scientists (Yore, Hand, & Florence, 2004; Duschl & Hamilton, 1992; Laudan, 1997; Russell, 1965).”
The Automation of Science (2009, background): “Scientific knowledge is best expressed using formal logical languages (3).”
How Is Physical Literacy Defined? A Contemporary Update (2018, background): “Generally, good science is embodied by debate, discussion, and a willingness to evolve and progress ideas (Popper, 1957) and, in this respect, physical literacy is thriving.”
Estimating Population Mean Power Under Conditions of Heterogeneity and Selection for Significance (2020, background): “Thus, if reproducibility is a requirement of good science (Bunge, 1998; Popper, 1959), it follows that high statistical power is a necessary condition for quality science.”