After decades of debates about species concepts, there is broad agreement that species are evolving lineages. However, species classification is still in a state of disorder: different methods of delimitation lead to competing outcomes for the same organisms, and the groups recognised as species are of widely different kinds. This paper considers whether this problem can be resolved by developing a unitary scale for evolutionary independence. Such a scale would show clearly when groups are comparable and allow taxonomists to choose (...) a conventional threshold of independence for species status. Existing measurement approaches to species delimitation are typically shot down by what I call the heterogeneity objection, according to which independently evolving groups are too heterogeneous to be captured by a single scale. I draw a parallel with the measurement of temperature to argue that this objection does not provide sufficient reasons to abandon the measurement approach, and that such an approach may even help to make the vague notion of evolutionary independence more precise. (shrink)

It is well known that taxonomists rely on many different methods and criteria for species delimitation, leading to different kinds of groups being recognised as species. While this state of relative disorder is widely acknowledged, there is no similar agreement about how it should be resolved. This paper considers the view that the disorder in species classification should be resolved by a system of taxonomic governance. I argue that such a system of governance is best seen as a combination of (...) standardisation, unification and regulation, each of which can be implemented in different forms. I investigate the forms that these three components should take for taxonomic governance by looking into two successfully governed classification systems, namely, virus classification and enzyme classification. The last part of the paper then defends the governance view against five objections. (shrink)

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)

Scientific activities strongly depend on concepts and classifications to represent the world in an orderly and workable manner. This creates a trade-off. On the one hand, it is important to leave space for conceptual and classificatory criticism. On the other hand, agreement on which concepts and classifications to use, is often crucial for communication and the integration of research and ideas. In this paper, we show that this trade-off can sometimes best be resolved through conceptual governance, in which scientific institutions (...) set a collective conceptual standard, and that voting can be a reasonable way to implement that governance. Voting is a means to aggregate among conflicting values, preferences and priorities that often underpin conceptual or classificatory debates, all while signaling ongoing disagreement. We also discuss how the legitimacy of the voting process and its outcome can be ensured. (shrink)