Brandon Boesch

Our first gaze into Black Mirror, “The National Anthem,” forces us, the viewers, to think about how moral obligations should be weighed against other competing obligations and also to examine our desire to see spectacle. In this chapter we discuss the three main ways in which philosophers think about moral obligations and examine how these ethical theories are employed by different individuals and groups in the episode. We also discuss how this particular episode aligns with Black Mirror's general message about the dangers of technology and how disgust is being invoked and employed to play a moral role by helping the viewer recognize the moral depravity found in the modern obsession with spectacle.

Previously, I (Boesch 2017) described a notion called “representational licensing”—the set of activities of scientific practice by which scientists establish the intended representational use of a vehicle. In this essay, I expand and develop this concept of representational licensing. I begin by showing how the concept is of value for both pragmatic and substantive approaches to scientific representation. Then, through the examination of a case study of the Mississippi River Basin Model, I point out and explain some of the activities of representational licensing that help to establish the representational nature of this model. Throughout the exploration of the case study, I pause to identify some important lessons which apply more generally about the nature of representational licensing in science.

In this essay, I examine the role of dissimilarity in scientific representation. After briefly reviewing some of the philosophical literature which places a strong emphasis on the role of similarity, I turn to examine some work from Carroll and Borges which demonstrates that perfect similarity is not valuable in the representational use of maps. Expanding on this insight, I go on to argue that this shows that dissimilarity is an important part of the representational use of maps—a point I then extend to the case of scientific representation. Relying on some work from Latour, I argue that dissimilarity plays an essential role in representational practice, by providing novel forms of manipulation and use which affords the achievement of various epistemic and nonepistemic aims. After showing how this point connects to some other literature on scientific representation, I discuss some examples of the value of dissimilarity in the use of representational vehicles. Overall, I argue that to understand scientific representation, we will need to consider more than just similarity. We will need to explore dissimilarities as well.

Agent-based accounts of scientific representation all agree that the representational relationship is constituted by the actions of scientists. Despite this agreement, there are several differences in how agent-based accounts describe scientific representation. In this essay, I argue that these differences do not undercut the compatibility between the accounts. I make my argument by examining the nature of human agency and demonstrating that scientific, representational actions are multiply describable. I then argue that the differences between the accounts are valuable because they help to bring different parts of the representational practices of science into greater focus.

While many recent accounts of scientific representation have given a central role to the agency and intentions of scientists in explaining representation, they have left these agential concepts unanalyzed. An account of scientific, representational actions will be a useful piece in offering a more complete account of the practice of representation in science. Drawing on an Anscombean approach to the nature of intentional actions, the Means-End Account of Scientific, Representational Actions describes three features of scientific, representational actions: (I) the final description in the means-end ordering of descriptions is some scientific aim; (II) that interaction with a vehicle distinct from its target stands as an earlier description which is ordered toward the final description as means to end; and (III) the means-end structure is licensed by scientific practice. After describing each of the components of the Means-End Account in greater detail through the example of the representational use of a mathematical model, I explain how it can demarcate scientific, representational actions from other sorts of actions. I close by describing some payoffs of the account, showing how it contributes to a more thorough understanding of the practice of representation in science and how it can be of use in understanding the close connection between representation and other forms of scientific activity.

It is widely argued that the skills of scientific expertise are tacit, meaning that they are difficult to study. In this essay, I draw on work from the philosophy of action about the nature of skills to show that there is another access point for the study of skills—namely, skill transmission in science education. I will begin by outlining Small’s Aristotelian account of skills, including a brief exposition of its advantages over alternative accounts of skills. He argues that skills exist in a sort of life cycle between learning, practicing, and transmitting, which provides reasons to think that we should pay close attention to the way skills are transmitted in teaching and learning. To demonstrate how a study of skill transmittance can be revealing about the nature of skills in expertise, I explore an example—what I identify as the skill of tension-balancing in model-building. After describing the skill, I briefly examine two case studies from the science education literature that reveal insights about the skill of tension-balancing as it functions in the practice of model-building.

In this essay, I will expand the philosophical discussion about the representational practice in science to examine its role in science education through four case studies. The cases are of what I call ‘educational laboratory experiments’, performative models used representationally by students to come to a better understanding of theoretical knowledge of a scientific discipline. The studies help to demonstrate some idiosyncratic features of representational practices in science education, most importantly a lack of novelty and discovery built into the ELEs as their methodology is solidified when it becomes a widely spread educational tool within a discipline. There is thus an irreducible role for the historical development of ELEs in understanding their representational nature and use. The important role of the historical development of ELEs leads to an interesting way that educators can use ELEs as a means of connecting students to important historical developments within their disciplines.

Callender and Cohen argue that there is no need for a special account of the constitution of scientific representation. I argue that scientific representation is communal and therefore deeply tied to the practice in which it is embedded. The communal nature is accounted for by licensing, the activities of scientific practice by which scientists establish a representation. A case study of the Lotka-Volterra model reveals how licensure is a constitutive element of the representational relationship. Thus, any account of the constitution of scientific representation must account for licensing, meaning that there is a special problem of scientific representation.

While many recent accounts of scientific representation have given a central role to the agency and intentions of scientists in explaining representation, they have left these agential concepts unanalyzed. An account of scientific, representational actions will be a useful piece in offering a more complete account of the practice of representation in science. Drawing on an Anscombean approach to the nature of intentional actions, the Means-End Account of Scientific, Representational Actions describes three features of scientific, representational actions: the final description in the means-end ordering of descriptions is some scientific aim; that interaction with a vehicle distinct from its target stands as an earlier description which is ordered toward the final description as means to end; and the means-end structure is licensed by scientific practice. After describing each of the components of the Means-End Account in greater detail through the example of the representational use of a mathematical model, I explain how it can demarcate scientific, representational actions from other sorts of actions. I close by describing some payoffs of the account, showing how it contributes to a more thorough understanding of the practice of representation in science and how it can be of use in understanding the close connection between representation and other forms of scientific activity.

According to the work of Alasdair MacIntyre, there is an incommensurability between different theories of normative ethics. MacIntyre’s view on the incommensurability of ethical discourse casts doubt upon the pluralistic proposal of Magelssen and colleagues, since the insights gained from the various theories will themselves be incommensurate with one another. However, since there are obvious benefits provided both by arguments for pluralism and the insights of Magelssen and colleagues, I utilize some later work of MacIntyre to offer an alternative means of conceiving of the role of ethical theories not as providing direct insights, but rather as imagined perspectives which will give rise to practical knowledge for the clinical ethicists, which can be used in developing their conclusions.

DeMarco and colleagues present a compelling method of dealing with medical risks for which there is equipoise which might be implicated in a given research protocol. This commentary examines how the proposed model should inform the disclosure of other, non-medical risks. Since equipoise is a fairly unclear notion for non-medical risks (since there is little sense of professional uncertainty regarding these risks), this could lead to the inclusion of nearly unlimited non-medical risks. To account for these risks more reasonably, I will argue that a strong participant-researcher relationship is vital to determine if there are any particular non-medical risks that might be uncertainly implicated in a given research program.

The article constitutes a detailed overview of the most important background literature on the topic of scientific representation. It gives a detailed outline of many of the important philosophical accounts of scientific representation. The primary division is between substantive accounts and deflationary/pragmatic accounts. Objections to each type of account are considered. Insights from the literature on modelling are discussed along with an overview of some of the insights from the sociology of science.