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Classificatory Theory in Biology

  • Thematic Issue Article: The Meaning of “Theory” in Biology
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Abstract

Scientific classification has long been recognized as involving a specific style of reasoning and doing research, and as occasionally affecting the development of scientific theories. However, the role played by classificatory activities in generating theories has not been closely investigated within the philosophy of science. I argue that classificatory systems can themselves become a form of theory, which I call classificatory theory, when they come to formalize and express the scientific significance of the elements being classified. This is particularly evident in some of the classification practices used in contemporary experimental biology, such as bio-ontologies used to classify genomic data and typologies used to classify “normal” stages of development in developmental biology. In this paper, I explore some characteristics of classificatory theories and ways in which they differ from other types of scientific theories and other components of scientific epistemology, such as models and background assumptions.

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Notes

  1. In my interpretation of this view, any material product of research activities, ranging from artifacts such as photographs to symbols (e.g., numbers), can be considered as a piece of data as long as (1) it is taken to constitute potential evidence for a phenomenon, and (2) it is possible to disseminate it across a community of scientists (the manipulation of artifacts aimed at their dissemination is also identified by Rheinberger, with a nod to Bruno Latour’s views on immutable mobiles, as crucial to transforming “traces” into “data”; Rheinberger 2011, p. 344). It is important to note at this point that I am treating all data, no matter whether in a digital or in a material form, as material artifacts. Materiality is crucial to the portability of data; and indeed, I view digital artifacts as concrete objects, even if the physical constraints and resistance offered by virtual environments are different from those encountered in non-virtual situations (data in a digital format are only visible and manipulable via interaction with computer screens). This position, which I expand upon in forthcoming work, is compatible with Wendy Parker’s (2009, p. 488) idea of “computer experiments as, first and foremost, experiments on real material systems.”

  2. Bogen and Woodward were ambiguous about their definition of phenomena, which can be taken to denote features of the world as well as the labels given to those features by researchers (e.g., McAllister 1997). For the purposes of my discussion I am happy to maintain this ambiguity so that bio-ontologies can be interpreted as capturing real objects or the ways in which biologists describe those objects. The realism of bio-ontologies is hotly debated in applied ontology circles (e.g., Smiths and Ceusters 2010), and taking a position on this discussion is not relevant to my purposes here.

  3. The dynamism of these types of theories might be best captured with reference to John Dewey’s (1938) account of the process of inquiry, in which knowledge is continually constituted and recreated through the process of scientific investigation, and the very attempt to formalize knowledge into theories works as a map and as an enabling condition for such change.

  4. Krakauer et al. (2011, p. 272) note that “One of the vaunted benefits of machine learning is that classification and prediction tasks can be performed without insights into the structure and dynamics of the underlying system.” When considering bio-ontologies, one of the main motors of machine learning, as a form of theory, it is clear that this conceptualization of machine learning does not hold. Classification is a highly conceptual exercise, whose value and significance can only be assessed through knowledge of the underlying biological system.

  5. One could question the extent to which this generalization has been successfully achieved. While I do not see this as crucial to my argument, which concerns the underlying aspiration of this system to generalize rather than its success in doing so, I have discussed the successes and difficulties of this enterprise in Leonelli et al. (2011) and Leonelli (2012b).

  6. For a discussion of the role of grand theories in data-intensive science, see Callebaut (2012).

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Acknowledgments

This research was funded by the ESRC as part of the ESRC Centre for Genomics in Society. I warmly thank the following individuals for very helpful discussions: the editors of this special issue Massimo Pigliucci, Kim Sterelny, and Werner Callebaut; the participants in the KLI Workshop on “The Meaning of ‘Theory’ in Biology” (particularly Jim Griesemer) and the Biological Interest Groups at the University of Minnesota (particularly Alan Love and Bill Wimsatt) and the University of Exeter (particularly Staffan Müller-Wille, who also provided insightful comments on the draft; John Dupré; and Berris Charnley); and Maureen O’Malley, Thomas Reydon, Jane Lomax, Midori Harris, and James McAllister.

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  1. Department of Sociology and Philosophy, ESRC Centre for Genomics in Society, University of Exeter, Exeter, UK

    Sabina Leonelli

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Leonelli, S. Classificatory Theory in Biology. Biol Theory 7, 338–345 (2013). https://doi.org/10.1007/s13752-012-0049-z

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