The collection and classification of data into meaningful categories is a key step in the process of knowledge making. In the life sciences, the design of data discovery and integration tools has relied on the premise that a formal classificatory system for expressing a body of data should be grounded in consensus definitions for classifications. On this approach, exemplified by the realist program of the Open Biomedical Ontologies Foundry, progress is maximized by grounding the representation and aggregation of data on (...) settled knowledge. We argue that historical practices in systematic biology provide an important and overlooked alternative approach to classifying and disseminating data, based on a principle of coordinative rather than definitional consensus. Systematists have developed a robust system for referring to taxonomic entities that can deliver high quality data discovery and integration without invoking consensus about reality or “settled” science. (shrink)

The term “cladist” has distinct meanings in distinct contexts. Communication between philosophers, historians, and biologists has been hindered by different understandings of the term in various contexts. In this paper I trace historical and conceptual connections between several broadly distinct senses of the term “cladist”. I propose seven specific definitions that capture distinct contemporary uses. This serves to disambiguate some cases where the meaning is unclear, and will help resolve apparent disagreements that in fact result from conflicting understandings of the (...) term. (shrink)

In this paper, I show how idealizations contribute to social activities in science, such as the recruitment of experts to a research project. These contributions have not been explicitly discussed by recent philosophical accounts of scientific idealization. These accounts have focused on how idealizations influence activities like scientific theorization, explanation, and modeling. Other accounts focus on how idealizations influence policy-making and science communication. I expand these accounts by exploring the uses of idealized phylogenetic trees in science. Trees are not only (...) useful for improving our understanding and public communication of evolutionary history, but they also help with the organization of laboratories and collaboration among scientists. Attending to the relation between idealizations and these social practices in science matters. It can help us understand why idealized models become entrenched and why certain social practices change over time. (shrink)

This paper examines the history of animal behavior studies after the synthesis period. Three episodes are considered: the adoption of the theory of natural selection, the mathematization of ideas, and the spread of molecular methods in behavior studies. In these three episodes, students of behavior adopted practices and standards developed in population ecology and population genetics. While they borrowed tools and methods from these fields, they made distinct uses that set them relatively apart and led them to contribute, in their (...) own way, to evolutionary theory. These episodes also highlight some limitations of “conjunction narratives” centered on the relation between a discipline and the modern synthesis. A trend in conjunction narratives is to interpret any development related to evolution in a discipline as an “extension,” an “integration,” or as a “delayed” synthesis. I here suggest that this can lead to underestimate discontinuities in the history of evolutionary biology. (shrink)

Big data is opening new angles on old questions about scientific progress. Is scientific knowledge cumulative? If yes, how does it make progress? In the life sciences, what we call the Consensus Principle has dominated the design of data discovery and integration tools: the design of a formal classificatory system for expressing a body of data should be grounded in consensus. Based on current approaches in biomedicine and systematic biology, we formulate and compare three types of the Consensus Principle: realist, (...) contextual-best, and coordinative. Contrasted with the realist program of the Open Biomedical Ontologies Foundry, we argue that historical practices in systematic biology provide an important and overlooked alternative based on coordinative consensus. Systematists have developed a robust system for referring to taxonomic entities that can deliver high quality data discovery and integration without invoking consensus about reality or “settled” science. (shrink)