Public participation in scientific research has gained prominence in many scientific fields, but the theory of participatory research is still limited. In this paper, we suggest that the divergence of values and goals between academic researchers and public participants in research is key to analyzing the different forms this research takes. We examine two existing characterizations of participatory research: one in terms of public participants' role in the research, the other in terms of the virtues of the research. In our (...) view, each of these captures an important feature of participatory research but is, on its own, limited in what features it takes into account. We introduce an expanded conception of norms of collaboration that extends to both academic researchers and public participants. We suggest that satisfying these norms requires consideration of the two groups' possibly divergent values and goals, and that a broad characterization of participatory research that starts from participants' values and goals can motivate both public participants’ role in the research and the virtues of the research. The resulting framework clarifies the similarities and differences among participatory projects and can help guide the responsible design of such projects. (shrink)

Astrophysics faces methodological challenges as a result of being a predominantly observation-based science without access to traditional experiments. In light of these challenges, astrophysicists frequently rely on computer simulations. Using collisional ring galaxies as a case study, I argue that computer simulations play three roles in reasoning in astrophysics: (1) hypothesis testing, (2) exploring possibility space, and (3) amplifying observations.

A central goal of the scientific endeavor is to explain phenomena. Scientists often attempt to explain a phenomenon by way of representing it in some manner—such as with mathematical equations, models, or theory—which allows for an explanation of the phenomenon under investigation. However, in developing scientific representations, scientists typically deploy simplifications and idealizations. As a result, scientific representations provide only partial, and often distorted, accounts of the phenomenon in question. Philosophers of science have analyzed the nature and function of how (...) scientists construct representations, deploy idealizations, and provide explanations. As such, our aim in this special issue is to bring these three pillars of research into closer contact with the contributions to it focusing on three main themes. -/- The first set of papers, Alan Baker (2021) and Marc Lange (2021), address mathematical explanations in science. Baker (2021), a proponent of mathematical Platonism, examines its capacity to evade the critique that the so-called Enhanced Indispensability Argument is circular. Lange (2021) examines distinctively mathematical explanations, arguing that neither Platonism nor representationalism are successful paths, and instead argues in favor of Aristotelian realism. A second theme emerging from the papers in this special issue is the impact that various conceptualizations of idealization have on our abilities to offer scientific explanations, to pro- duce an analysis of what explanations are or should be, and to understand scientific representation. Peter Tan (2021) suggests amending inferentialist accounts of scientific representation to account for inconsistent idealizations. Michael Strevens (2021) advocates a view wherein the introduction of idealizations into a model is legitimate so long as it pertains to non-difference-making factors, arguing for a logical reading of the notion of difference-making. Natalia Carrillo and Tarja Knuuttila (2022) offer an alternative account to the idealization-as-distortion view, emphasizing instead the holistic nature of idealization. -/- Finally, contributions by Carrillo and Knuuttila (2022), Terzian (2021), Valente (2021), and Rodriguez (2021) illustrate how issues regarding idealization, representation, and explanation are applied to specific contexts and across various sciences. Carrillo and Knuuttila (2022) examine conceptions of idealization in the context of models of the nerve impulse. Giulia Terzian (2021) extends the discussion of idealizations to the context of generative linguistics. Giovanni Valente (2021) examines how idealizations and evaluations of accurate representation impact capacities to explain phenomena in the context of statistical thermodynamics. Quentin Rodriguez (2021) examines the role of idealizations and analogies in various strategies to explain critical phenomena. -/- In what follows, we offer a brief overview of important philosophical issues connected to representation, idealization, and explanation in science. We then provide short summaries of the eight papers in this special issue. (shrink)

The debate between ΛCDM and MOND is often cast in terms of competing gravitational theories. However, recent philosophical discussion suggests that the ΛCDM–MOND debate demonstrates the challenges of multiscale modeling in the context of cosmological scales. I extend this discussion and explore what happens when the debate is thought to be about modeling rather than about theory, offering a model-focused interpretation of the ΛCDM–MOND debate. This analysis shows how a model-focused interpretation of the debate provides a better understanding of challenges (...) asso- ciated with extension to a different scale or domain, which are tied to commitments about explanatory fit. (shrink)

Gravitational interactions allowed astronomers to conclude that dark matter rings all luminous galaxies in gigantic halos, but this only accounts for a fraction of the total mass of dark matter believed to exist. Where is the rest? We hypothesize that some of it resides in dark galaxies, pure dark matter halos that either never possessed or have totally lost their baryonic matter. This article explores methodological challenges that arise because of the nature of observation in astrophysics and examines how the (...) blend of observation, simulation, and theory we call the Observing the Invisible approach might make detecting such dark objects possible. (shrink)

In philosophy, much attention has rightly been paid to the need to diversify teaching with regard to who teaches, who is taught, and which authors and questions are the focus of study. Less attention, however, has been paid to inclusive pedagogy—the teaching methods that are used, and how they can make or fail to make classes as accessible as possible to the diverse students who enter them. By drawing on experiences from our own teaching as well as research on student-centered, (...) inclusive best practices, we advocate for five principles of inclusive pedagogy: fostering a growth mindset, examining inclusive conceptions of authority, promoting transparency, encouraging flexibility, and, finally, continually promoting self-reflection for both students and teachers. (shrink)

In philosophy, much attention has rightly been paid to the need to diversify teaching with regard to who teaches, who is taught, and which authors and questions are the focus of study. Less attention, however, has been paid to inclusive pedagogy—the teaching methods that are used, and how they can make or fail to make classes as accessible as possible to the diverse students who enter them. By drawing on experiences from our own teaching as well as research on student-centered, (...) inclusive best practices, we advocate for five principles of inclusive pedagogy: fostering a growth mindset, examining inclusive conceptions of authority, promoting transparency, encouraging flexibility, and, finally, continually promoting self-reflection for both students and teachers. (shrink)

The problem of inadequate professional training for graduate students in teaching and pedagogy has recently come into sharp relief. Pro- viding teacher training for philosophy graduate students through for-credit courses has been recommended as a solution to this problem. This paper provides an overview of the problem, identifies several aims such a course should have, and provides a detailed overview of a course satisfying those aims. By providing a detailed outline of the course, this paper can act as a resource (...) for faculty tasked with teaching such a course. Finally, we justify the pedagogical decisions made in the design of this course to prepare fa- cilitators to more effectively teach it, to allow facilitators to make informed and intentional decisions when adapting the course to their program, and as a demonstration of what we take to be some of the best practices in teach- ing and pedagogy. That is, the design of the course is informed by the very material covered in the course. (shrink)

Empirical data show that members of underrepresented and historically marginalized groups in academia undertake many forms of undervalued or unnoticed labor. While the data help to identify that this labor exists, they do not provide a thick description of what the experience is like, nor do they offer a framework for understanding the different kinds of invisible labor that are being undertaken. We identify and analyze a distinct, undervalued, and invisible labor that the data have left unnamed and unmeasured: ontological (...) labor, the work required to manage one’s identity and body if either or both do not fit into academic structures, norms, and demands. We argue that ontological labor efforts should be understood as a form of labor. We then provide a characterization of ontological labor, detailing the labor as navigating one’s obligations to give and managing entitlements to take. We also highlight the ontological labor that takes place through instances of resistance, such as through complaint or refusals. (shrink)

This chapter examines some of the philosophical literature on idealizations in science and the epistemic challenges idealizations potentially pose for astrophysical methodology, particularly its use of computer simulations. We begin by surveying philosophical literature on idealization connected to (1) kinds of idealizations deployed in science, (2) the aims of idealization in science, and (3) various strategies for de-idealization. Using collisional ring galaxy simulations as a case study, we examine how these three themes play out in the context of astrophysical computer (...) simulations. Ultimately, we argue that deploying deidealization strategies is central to bolstering epistemic confidence in simulations in astrophysics. We conclude with some remarks on the role of idealization in the context of astrophysical computer simulations more generally. (shrink)

Margaret Morrison, (2015) Reconstructing Reality: Models, Mathematics, and Simulations. Oxford University Press, New York. -/- Scientific models, mathematical equations, and computer simulations are indispensable to scientific practice. Through the use of models, scientists are able to effectively learn about how the world works, and to discover new information. However, there is a challenge in understanding how scientists can generate knowledge from their use, stemming from the fact that models and computer simulations are necessarily incomplete representations, and partial descriptions, of their (...) target systems. In order to construct a model, one must make idealizations, approximations, and abstractions. Given these constraints in constructing models, there is a question of whether they can provide new insight into how real systems actually work. So, how is it that highly abstract models inform us about the nature of the world, and more specifically, how do they provide explanatory knowledge? In Reconstructing Reality: Models, Mathematics, and Simulations, philosopher Margaret Morrison undertakes this task of ... (shrink)

A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...) models. I present and critically assess two leading accounts of model assessment, arguing that neither is sufficient to provide a complete understanding of model evaluation. I introduce a more powerful framework incorporating elements of the two views, which can help answer these three questions: What is the target of evaluation in model assessment? How does that evaluation proceed? What licenses us in making inferences about the real world, based on the evaluation of our models as successful? The framework identifies two distinct targets of model evaluation: representational similarity between the model and target system, and the adequacy of the model as a tool to answer questions. Both assessments must be relativized to a purpose, of which there are three general kinds: descriptive, predictive, and explanatory. These purposes differ in the way they inform the similarity relation, which is relevant for the similarity assessment, and the output they produce, which is relevant for the adequacy assessment. Any model can be assessed relative to any purpose, however a model encodes certain decisions made during the model’s construction, which impact its ability to be applied to a new purpose or new domain. My framework shows that extending a model, and drawing inferences from it, depends on its representational similarity. I apply this framework to several examples taken from astrophysics showing in detail how it can help illuminate the structure of the models, as well as make the justification for inferences made from them clear. The final chapter is a detailed analysis of a contemporary debate surrounding the use of models in astrophysics, between proponents of MOND and the standard ΛCDM model. (shrink)