Biodiversity informatics produces global biodiversity knowledge through the collection and analysis of biodiversity data using informatics techniques. To do so, biodiversity informatics relies upon data accrual, standardization, transferability, openness, and “invisible” infrastructure. What biodiversity informatics mean to society, however, cannot be adequately understood without recognizing what organizes biodiversity data. Using insights from science and technology studies, we story the organizing “visions” behind the growth of biodiversity informatics infrastructures in Sweden—an early adopter of digital technologies and significant contributor to global biodiversity (...) data—through interviews, scientific literature, governmental reports and popular publications. This case story discloses the organizational formation of Swedish biodiversity informatics infrastructures from the 1970s to the present day, illustrating how situated perspectives or “visions” shaped the philosophies, directions and infrastructures of its biodiversity informatics communities. Specifically, visions related to scientific progress and species loss, their institutionalization, and the need to negotiate external interests from governmental organizations led to unequal development across multiple infrastructures that contribute differently to biodiversity knowledge. We argue that such difference highlights that the social and organizational hurdles for combining biodiversity data are just as significant as the technological challenges and that the seemingly inconsequential organizational aspects of its infrastructure shape what biodiversity data can be brought together, modelled and visualised. (shrink)

The objective of this paper is to balance two major conceptual tendencies in science policy studies, continuity and discontinuity theory. While the latter argue for fundamental and distinct changes in science policy in the late 20th century, continuity theorists show how changes do occur but not as abrupt and fundamental as discontinuity theorists suggests. As a point of departure, we will elaborate a typology of scientific governance developed by Hagendijk and Irwin ( 2006 ) and apply it to new empirical (...) material. This makes possible a contextualization of the governance of science related to the codification of the “third assignment” of the Swedish higher education law of 1977. The law defined the relation between university science and Swedish citizens as a dissemination project, and did so despite that several earlier initiatives actually went well beyond such a narrow conceptualisation. Our material reveals continuous interactive and rival arrangements linking the state, public authorities, the universities and private industrial enterprises. We show how different but coexisting modes of governance of science existed in Sweden during the 20th century, in clear contrast with the picture promoted by discontinuity theorists. A close study of the historical development suggests that there were several periods of layered governance when interactions and dynamics associated with continuity as well as discontinuity theories were prevalent. In addition, we conclude that the typology of governance applied in the present paper is fruitful for carrying out historical analyses of the kind embarked upon in spite of certain methodological shortcomings. (shrink)

Citizen science projects have started to utilize Machine Learning to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the results of 12 citizen science projects that used machine learning to optimize classification tasks. We examined the distribution of tasks between (...) citizen scientists, experts, and algorithms, and how epistemic agency was enacted in terms of whose knowledge shapes the distribution of tasks, who decides what knowledge is relevant to the classification, and who validates it. In our descriptive results, we found that experts, who include professional scientists and algorithm developers, are involved in every aspect of a project, from annotating or labelling data to giving data to algorithms to train them to make decisions from predictions. Experts also test and validate models to improve their accuracy by scoring their outputs when algorithms fail to make correct decisions. Experts are mostly the humans involved in a loop, but when algorithms encounter problems, citizens are also involved at several stages. In this paper, we present three main examples of citizens-in-the-loop: when algorithms provide incorrect suggestions; when algorithms fail to know how to perform classification; and when algorithms pose queries. We consider the implications of the emphasis on optimization on the ideal of science and the role of citizen scientists from a perspective informed by Science and Technology Studies and Information Systems. Based on our findings, we conclude that ML in CS classification projects, far from being deterministic in its nature and effects, may be open to question. There is no guarantee that these technologies can replace citizen scientists, nor any guarantee that they can provide citizens with opportunities for more interesting tasks. (shrink)