https://cspo.org/legacy/library/110104F2FV_lib_SarewitzEnvSciPo.pdf

How science makes environmental controversies worse

/pdf/explanation-a-mechanist-alternative-3nfmm4i1c9.pdf

Explanation: a mechanist alternative.

A National Research Council report, Scientific Research in Education, has elicited considerable criticism from the education research community, but this criticism has not focused on a key assumption of the report—its Humean, regularity conception of causality. It is argued that this conception, which also underlies other arguments for “scientifically-based research,” is narrow and philosophically outdated, and leads to a misrepresentation of the nature and value of qualitative research for causal explanation. An alternative, realist approach to causality is presented that supports the scientific legitimacy of using qualitative research for causal investigation, reframes the arguments for experimental methods in educational research, and can support a more productive collaboration between qualitative and quantitative researchers.

Causal Explanation, Qualitative Research, and Scientific Inquiry in Education

Representing and Intervening

Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language‐like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms “law” and “theory,” I distinguish principles, specific conditions, models, hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations.

/pdf/how-models-are-used-to-represent-reality-5dxqzepi2c.pdf

How Models Are Used to Represent Reality

Recent work in the philosophy of science has generated an apparent conflict between theories attempting to explicate the nature of scientific representation. On one side, there are what one might call ‘informational’ views, which emphasize objective relations (such as similarity, isomorphism, and homomorphism) between representations (theories, models, simulations, diagrams, etc.) and their target systems. On the other side, there are what one might call ‘functional’ views, which emphasize cognitive activities performed in connection with these targets, such as interpretation and inference. The main sources of the impression of conflict here are arguments by some functionalists to the effect that informational theories are flawed: it is suggested that relations typically championed by informational theories are neither necessary nor sufficient for scientific representation, and that any theory excluding functions is inadequate. In this paper I critically examine these arguments, and contend that, as it turns out, informational and functional theories are importantly complementary.

Informational versus functional theories of scientific representation

What is Scientific Realism

Minimally, causal realism (as understood here) is the view that accounts of causation in terms of mere, regular or probabilistic conjunction are unsatisfactory, and that causal phenomena are correctly associated with some form of de re necessity. Classic arguments, however, some of which date back to Sextus Empiricus and have appeared many times since, including famously in Russell, suggest that the very notion of causal realism is incoherent. In this paper I argue that if such objections seem compelling, it is only because everyday expressions concerning causal phenomena are misleading with respect to certain metaphysical details. These expressions generally make reference to the relations of events or states of affairs, but ignore or obscure the role played by causal properties. I argue that on a proposed alternative, an analysis in terms of causal processes, more refined descriptions of causal phenomena escape the charge of incoherence. Causal necessity is here located in the relations of causal properties. I distinguish this view from the recent process theories of Salmon and Dowe, which are disinterested in causal realism.

Causal Realism: Events and Processes

Realists regarding scientific knowledge-those who think that our best scientific representations truly describe both observable and unobservable aspects of the world-have special need of a notion of approximate truth. Since theories and models are rarely considered true simpliciter, the realist requires some means of making sense of the claim that they may be false and yet close to the truth, and increasingly so over time. In this paper, I suggest that traditional approaches to approximate truth pay insufficient attention to an important distinction between two features of scientific knowledge, and that for each of them, analogies between representational practices in the sciences and in art prove useful to understanding how this situation can be remedied. First, I outline two distinct ways in which representations deviate from the truth, commonly referred to as "abstraction" and "idealization". Second, I argue that these practices exemplify different conventions of representation, and that for each, the conditions of approximation relevant to explicating the concept of approximate truth must be understood differently. The concept is thus heterogeneous; approximate truth is a virtue that is multiply realized, relative to different contexts of representation. This understanding is facilitated, I suggest, by considering the distinction between realistic and non-realistic representation in art.

/pdf/truth-and-representation-in-science-two-inspirations-from-2vr1fof1ed.pdf

Truth and Representation in Science: Two Inspirations from Art

Scientific Ontology: Integrating Naturalized Metaphysics and Voluntarist Epistemology contends that ontological commitments associated with scientific inquiry are infused with philosophical commitments. Interpretations of scientific ontology involve (what I call) metaphysical inferences, and furthermore, there are different ways of making these inferences, on the basis of different but nonetheless rational epistemic stances. If correct, this problematizes any neat distinction between naturalized and other metaphysics, and dissolves any presumption of there being a uniquely correct answer to ontological questions connected to the sciences. In this paper, I consider some weighty challenges to these contentions by Amanda Bryant, Stathis Psillos, and Matthew Slater.

Risk, Reward, and Scientific Ontology: Reply to Bryant, Psillos, and Slater

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