On the pursuitworthiness of qualitative methods in empirical philosophy of science

While the pursuitworthiness of philosophical ideas has changed over time, philosophical practice and methodology have not kept pace. The worthiness of a philosophical pursuit includes not only the ideas and objectives one pursues but also the methods with which one pursues them. In this paper, we articulate how empirical approaches benefit philosophy of science, particularly advocating for the use of qualitative methods for understanding the social and normative aspects of scientific inquiry. After situating qualitative methods within empirical philosophy of science, we discuss how to adapt these traditionally sociological methods to empirically inform philosophical questions. Our aim is to normalize and legitimize qualitative methods for philosophical purposes and discuss how they can elucidate descriptive and normative components of scientific practice in a more generalizable non-idealized manner. We contend that qualitative methods are particularly well suited to philosophical interest in the social norms of science, their achievability, and their mutability. Furthermore, unlike more historical case studies in philosophy, qualitative methods enable more confidence in generalizability, albeit limited, from a concrete sample to a larger class. We conclude by addressing anxieties about the distinctness of empirical philosophy of science from social epistemology and from sociology of science.

Clarifying how social epidemiological research constructs the category of low socioeconomic status: A response to Kamphuis et al

In our previous article, published in this journal, we argued that epidemiology has a constructive role with regard to socioeconomic health inequalities. We concluded that, as long as the construction of LSES remains unquestioned, social epidemiology might continue to (re)produce what it examines: LSES populations 'known to be unhealthy'. Recently, in this journal, Kamphuis et al. responded to our article. While they welcomed our reflections, the authors also posed critique to our arguments. In this paper, we respond to that critique and deepen the discussion on the use of (L)SES categories in social epidemiology. For this purpose, we further clarify our arguments and state that in health inequality research it is important to: 1) Pay attention to the unintended effects of research; 2) Consider the origin and effects of explanatory concepts; and 3) reflect on the norms of cultural capital.

A transformation of Bayesian statistics:Computation, prediction, and rationality

Bayesian approaches have long been a small minority group in scientific practice, but quickly acquired a high level of popularity since the 1990s. This paper shall describe and analyze this turn. I argue that the success of Bayesian approaches hinges on computational methods that make a class of models predictive that would otherwise lack practical relevance. Philosophically, however, this orientation toward prediction comes at a price. The new computational approaches change Bayesian rationality in an important way. Namely, they undercut the interpretation of priors, turning them from an expression of beliefs held prior to new evidence into an adjustable parameter that can be manipulated flexibly by computational machinery. Thus, in the case of Bayes, one can see a coevolution of computing technology, an exploratory-iterative mode of prediction, and the conception of rationality.

Process epistemology in the COVID-19 era: rethinking the research process to avoid dangerous forms of reification

Whether we live in a world of autonomous things, or a world of interconnected processes in constant flux, is an ancient philosophical debate. Modern biology provides decisive reasons for embracing the latter view. How does one understand the practices and outputs of science in such a dynamic, ever-changing world - and particularly in an emergency situation such as the COVID-19 pandemic, where scientific knowledge has been regarded as bedrock for decisive social interventions? We argue that key to answering this question is to consider the role of the activity of reification within the research process. Reification consists in the identification of more or less stable features of the flux, and treating these as constituting stable things. As we illustrate with reference to biological and biomedical research on COVID-19, reification is a necessary component of any process of inquiry and comes in at least two forms: (1) means reification (phenomena-to-object), when researchers create objects meant to capture features of the world, or phenomena, in order to be able to study them; and (2) target reification (object-to-phenomena), when researchers infer an understanding of phenomena from an investigation of the epistemic objects created to study them. We note that both objects and phenomena are dynamic processes and argue that have no reason to assume that changes in objects and phenomena track one another. We conclude that failure to acknowledge these forms of reification and their epistemic role in scientific inquiry can have dire consequences for how the resulting knowledge is interpreted and used.

Beyond descriptive accuracy: The central dogma of molecular biology in scientific practice

There is no denying the Central Dogma's impact on the biological sciences. Since the Dogma's formulation by Francis Crick in 1958, however, many have debated the Dogma's empirical adequacy. My aim is to move beyond these discussions, and instead consider the Central Dogma's significance to contemporary biological practice. To do this, I consider four distinct approaches for determining the non-descriptive methodological significance of a scientific principle. I argue that these approaches fail to vindicate the Central Dogma, and that, under many of these approaches, the Dogma amounts to a triviality.

A secure procedure for early career scientists to report apparent misconduct

Early career scientists sometimes observe senior scientists engage in apparent scientific misconduct, but feel powerless to intervene, lest they imperil their careers. We propose a Secure Reporting Procedure that both protects them, when pursuing those concerns, and treats the senior scientists fairly. The proposed procedure is, we argue, consistent with the ethical principles of the scientific community, as expressed in the codes of its professional organizations. However, its implementation will require changes in procedures and regulations. Those efforts will be a small price to pay for protecting the scientific community's integrity and fidelity to its principles. We begin by describing the circumstances motivating the proposal, then sketch its design, and, finally, illustrate next steps in its application in two national settings.

From successful measurement to the birth of a law: Disentangling coordination in Ohm's scientific practice

In this paper, I argue for a distinction between two scales of coordination in scientific inquiry, through which I reassess Georg Simon Ohm's work on conductivity and resistance. Firstly, I propose to distinguish between measurement coordination, which refers to the specific problem of how to justify the attribution of values to a quantity by using a certain measurement procedure, and general coordination, which refers to the broader issue of justifying the representation of an empirical regularity by means of abstract mathematical tools. Secondly, I argue that the development of Ohm's measurement practice between the first and the second experimental phase of his work involved the change of the measurement coordination on which he relied to express his empirical results. By showing how Ohm relied on different calibration assumptions and practices across the two phases, I demonstrate that the concurrent change of both Ohm's experimental apparatus and the variable that Ohm measured should be viewed based on the different form of measurement coordination. Finally, I argue that Ohm's assumption that tension is equally distributed in the circuit is best understood as part of the general coordination between Ohm's law and the empirical regularity that it expresses, rather than measurement coordination.

Setting scientific names at all taxonomic ranks in italics facilitates their quick recognition in scientific papers

It is common practice in scientific journals to print genus and species names in italics. This is not only historical as species names were traditionally derived from Greek or Latin. Importantly, it also facilitates the rapid recognition of genus and species names when skimming through manuscripts. However, names above the genus level are not always italicized, except in some journals which have adopted this practice for all scientific names. Since scientific names treated under the various Codes of nomenclature are without exception treated as Latin, there is no reason why names above genus level should be handled differently, particularly as higher taxon names are becoming increasingly relevant in systematic and evolutionary studies and their italicization would aid the unambiguous recognition of formal scientific names distinguishing them from colloquial names. Several leading mycological and botanical journals have already adopted italics for names of all taxa regardless of rank over recent decades, as is the practice in the International Code of Nomenclature for algae, fungi, and plants, and we hereby recommend that this practice be taken up broadly in scientific journals and textbooks.

Diagramming Evolution: The Case of Darwin's Trees

From his earliest student days through the writing of his last book, Charles Darwin drew diagrams. In developing his evolutionary ideas, his preferred form of diagram was the tree. An examination of several of Darwin's trees-from sketches in a private notebook from the late 1830s through the diagram published in the Origin-opens a window onto the role of diagramming in Darwin's scientific practice. In his diagrams, Darwin simultaneously represented both observable patterns in nature and conjectural narratives of evolutionary history. He then brought these natural patterns and narratives into dialogue, allowing him to explore whether the narratives could explain the patterns. But Darwin's diagrams did not reveal their meaning directly to passive readers; they required readers to engage dynamically with them in order to understand the connections they disclosed between patterns and narratives. Moreover, the narratives Darwin depicted in his diagrams did not represent past sequences of events that he claimed had actually occurred; the narratives were conjectural, schematic, and probabilistic. Instead of depicting actual histories in all their particularity, Darwin depicted narratives in his diagrams in order to make general claims about how nature works. The conjunction of these features of Darwin's diagrams is central to how they do their epistemic work.

Communicating with scientific graphics: A descriptive inquiry into non-ideal normativity

Scientists' graphical practices have recently become a target of inquiry in the philosophy of science, and in the cognitive sciences. Here I supplement our understanding of graphical practices via a case study of how researchers crafted the graphics for scientific publication in the field of circadian biology. The case highlights social aspects of graphical production which have gone understudied - especially concerning the negotiation of publication. I argue that it also supports a challenge to the claim that empirically-informed "cognitive design principles" offer an apt understanding of the norms of success which govern good scientific graphic design to communicate data and hypotheses to other experts. In this respect, the case-study also illustrates how "descriptive" studies of scientific practice can connect with normative issues in philosophy of science, thereby addressing a central concern in recent discussions of practice-oriented philosophy of science.

Repertoires: A post-Kuhnian perspective on scientific change and collaborative research

We propose a framework to describe, analyze, and explain the conditions under which scientific communities organize themselves to do research, particularly within large-scale, multidisciplinary projects. The framework centers on the notion of a research repertoire, which encompasses well-aligned assemblages of the skills, behaviors, and material, social, and epistemic components that a group may use to practice certain kinds of science, and whose enactment affects the methods and results of research. This account provides an alternative to the idea of Kuhnian paradigms for understanding scientific change in the following ways: (1) it does not frame change as primarily generated and shaped by theoretical developments, but rather takes account of administrative, material, technological, and institutional innovations that contribute to change and explicitly questions whether and how such innovations accompany, underpin, and/or undercut theoretical shifts; (2) it thus allows for tracking of the organization, continuity, and coherence in research practices which Kuhn characterized as 'normal science' without relying on the occurrence of paradigmatic shifts and revolutions to be able to identify relevant components; and (3) it requires particular attention be paid to the performative aspects of science, whose study Kuhn pioneered but which he did not extensively conceptualize. We provide a detailed characterization of repertoires and discuss their relationship with communities, disciplines, and other forms of collaborative activities within science, building on an analysis of historical episodes and contemporary developments in the life sciences, as well as cases drawn from social and historical studies of physics, psychology, and medicine.

Collaborative explanation and biological mechanisms

This paper motivates and outlines a new account of scientific explanation, which I term 'collaborative explanation.' My approach is pluralist: I do not claim that all scientific explanations are collaborative, but only that some important scientific explanations are-notably those of complex organic processes like development. Collaborative explanation is closely related to what philosophers of biology term 'mechanistic explanation' (e.g., Machamer et al., Craver, 2007). I begin with minimal conditions for mechanisms: complexity, causality, and multilevel structure. Different accounts of mechanistic explanation interpret and prioritize these conditions in different ways. This framework reveals two distinct varieties of mechanistic explanation: causal and constitutive. The two have heretofore been conflated, with philosophical discussion focusing on the former. This paper addresses the imbalance, using a case study of modeling practices in Systems Biology to reveals key features of constitutive mechanistic explanation. I then propose an analysis of this variety of mechanistic explanation, in terms of collaborative concepts, and sketch the outlines of a general theory of collaborative explanation. I conclude with some reflections on the connection between this variety of explanation and social aspects of scientific practice.

Collaborative explanation, explanatory roles, and scientific explaining in practice

Scientific explanation is a perennial topic in philosophy of science, but the literature has fragmented into specialized discussions in different scientific disciplines. An increasing attention to scientific practice by philosophers is (in part) responsible for this fragmentation and has put pressure on criteria of adequacy for philosophical accounts of explanation, usually demanding some form of pluralism. This commentary examines the arguments offered by Fagan and Woody with respect to explanation and understanding in scientific practice. I begin by scrutinizing Fagan's concept of collaborative explanation, highlighting its distinctive advantages and expressing concern about several of its assumptions. Then I analyze Woody's attempt to reorient discussions of scientific explanation around functional considerations, elaborating on the wider implications of this methodological recommendation. I conclude with reflections on synergies and tensions that emerge when the two papers are juxtaposed and how these draw attention to critical issues that confront ongoing philosophical analyses of scientific explanation.

Putting a spin on circulating reference, or how to rediscover the scientific subject

Bruno Latour claims to have shown that a Kantian model of knowledge, which he describes as seeking to unite a disembodied transcendental subject with an inaccessible thing-in-itself, is dramatically falsified by empirical studies of science in action. Instead, Latour puts central emphasis on scientific practice, and replaces this Kantian model with a model of "circulating reference." Unfortunately, Latour's alternative schematic leaves out the scientific subject. I repair this oversight through a simple mechanical procedure. By putting a slight spin on Latour's diagrammatic representation of his theory, I discover a new space for a post-Kantian scientific subject, a subject brilliantly described by Ludwik Fleck. The neglected subjectivities and ceaseless practices of science are thus re-united.