. Given the importance of the issue of scientific objectivity in our democratic societies and the significant development of citizen science, it is crucial to investigate how citizen science may either undermine or foster scientific objectivity. This paper identifies a variety of epistemic risks and benefits that participation of lay citizens in scientific inquiries may bring. It also discusses concrete actions and pending issues that should be addressed in order to foster objectivity in citizen science programs.

This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose (...) to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity. (shrink)

Keine ausführliche Beschreibung für "Suarez, Descartes, Spinoza, Hobbes, Locke, Newton" verfügbar.

The question of the division of cognitive labor has given rise to various models characterizing the way scientists should distribute their efforts. These models often consider the scientific community as a self-governed sphere constituted by rational agents making choices on the basis of fixed rules. Such models have recently been criticized for not taking into account the real mechanisms of science funding. Hence, the question of the utility of the DCL models in guiding science policy remains an open one. In (...) this paper, we show that two unconsidered dimensions would have to be taken into account. First, DCL studies miss the existence of distinct levels of epistemic objectives organizing the research process. Indeed, the scientific field is structured as a system of hierarchical, interconnected practices which are defined both by their inherent purposes and by various superposed external functions. Second, I criticize the absence of ontological considerations, since the epistemological significance of pluralism is highly dependent on the nature of the object under study. Because of these missing dimensions, current DCL models might have a limited usefulness to identify good practices of research governance. (shrink)