Studies in History and Philosophy of Science Part C-Studies in History and Philosophy of Biological and Biomedical Sciences最新文献

In 1948, American ornithologist Rodolphe Meyer de Schauensee began publishing what would be the most complete list of birds from Colombia that had ever been printed up to that time. His work was called The Birds of the Republic of Colombia (TBRC), and at the invitation of Armando Dugand, the director of the Instituto de Ciencias Naturales at the Universidad Nacional de Colombia and of the Caldasia journal, this work was exclusively published in the journal in five installments spanning four years. This paper analyzes the publishing aspects that particularly influenced the process of carrying out this work, with the objective of showing that scientific practices and publishing practices are not two absolutely separate domains. The circuit of communication present in TBRC's development is analyzed, specifically the efforts of the editor, printer and author to bring this work to fruition. This analysis demonstrates the following: (i) how the scientific interests of Meyer and the Instituto de Ciencias Naturales converge, (ii) the contradictions between scientific interests that promoted the publication of TBRC and the publishing rationale of a journal and (iii) how unforeseen publishing issues of the time, such as the increase in printing costs due to inflation, influenced the final structure of the work.

An analysis of arguments for pain eliminativism reveals two significant points of divergence between assumptions underlying biomedical research on pain and assumptions typically endorsed by eliminativist accounts. The first concerns the status of the term ‘pain,’ which is a description of a phenomenon, rather than an explanatory construct. The second concerns reductive explanation: pain is explained causally, in terms of mechanisms or factors that produce or determine it, rather than by identifying it with a physical structure, process or mechanism. These discrepancies undermine several arguments for pain eliminativism.

There has recently been an explosion of formal models of signaling, which have been developed in order to learn about different aspects of meaning. This paper discusses whether that success can also be used to provide an original naturalistic theory of meaning in terms of information or some related notion. In particular, it argues that, although these models can teach us a lot about different aspects of content, at the moment they fail to support the idea that meaning just is some kind of information. As an alternative, I suggest a more modest approach to the relationship between the informational notions used in models and semantic properties in the natural world.

Although concepts of disease have received much scrutiny, the benefits of distinguishing between theoretical and clinical disease—and what is meant by those terms—may not be as readily apparent. One way of characterizing the distinction between theoretical and clinical conceptions of disease is by relying on Boorse's biostatistical theory (BST) for a conception of theoretical disease. Clinical disease could then be defined as theoretical disease that is diagnosed. Explicating this distinction provides a useful extension of the BST. The benefits of this approach are clearly and non-normatively demarcating disease from non-disease, while allowing for values and purpose to determine what criteria are used in clinical practice to represent a disease's underlying dysfunction. Through discussion of a variety of medical conditions, including polycystic ovary syndrome and type 2 diabetes mellitus, I explore how the relationship between BST-based theoretical and clinical disease could make sense of various features of clinical practice and medical theory. It could do this by lending focus to a nuanced understanding of the pathophysiological defects present in disease and the means by which they are assessed. This could contribute to making sense of revised nosologies and diagnostic criteria.

How is scientific data used to represent phenomena and as evidence for claims about phenomena? In this paper, I propose that a specific type of claims – evidential claims – is involved in data practices to define and restrict the representational and evidential content of a dataset. I present an account of data practices in the epidemiology of the exposome based on the notion of evidential claims, which helps unpack the approaches, assumptions and warrants that connect different stages of research. I identify three different strategies to generate different types of evidential claims in this case. The macro strategy, which individuates the dataset that serves as the initial evidential space for research. The micro strategy, which is used to generate evidential claims about the microscopic and individual component of target phenomena. The association strategy, that uses evidence from the other strategies to identify a dataset as representation of the different levels and relations of exposure and disease. Differentiating between these strategies sheds light on the multi-faceted landscape of biomedical research on environment and health; and the roles of data and evidence in the process of inquiry.