The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose (...) a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe. (shrink)

This book is the culmination of the COST Action CA15212 Citizen Science to Promote Creativity, Scientific Literacy, and Innovation throughout Europe. It represents the final stage of a shared journey taken over the last 4 years. During this relatively short period, our citizen science practices and perspectives have rapidly evolved. In this chapter we discuss what we have learnt about the recent past of citizen science and what we expect and hope for the future.

Being a member of both local and global teams requires constant distribution and re-distribution of attention, engagement, and intensive communication over synchronous and asynchronous channels with remote and local partners. We explore in this paper the increasing number of social worlds such participants distribute their attention to, how this affects their level of engagement and attention, and how the workspace, collaboration technologies, and interaction modes afford and constrain the communicative events. The use of information and collaboration technologies (ICT) shapes and (...) reshapes work spaces, processes, and social interactions among team members, and team members reshape ICT and the way it is used. We use as a testbed the AEC Global Teamwork course established at Stanford in 1993 in collaboration with universities worldwide and provide examples from two sites—Stanford PBL Lab, California USA and Chalmers University, Goteborg Sweden. We used temporal analysis and qualitative methods of inquiry in order to study participants’ attention, how they used the site and the means, i.e., their social and material resources, to accomplish their interaction needs, and how they engaged throughout project reviews. (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)