Why do people engage in Citizen Science projects? The aim of this contribution is to explore the social mechanisms that push non-experts (i.e. citizens) to invest energy, time and (sometimes) money in collaborative initiatives on the ground of scientific research. Some relevant examples from the domain of community-based rain measuring are scrutinized, merging the views of a water scientist and a social scientist. After briefly discussing the limits of outdated approaches to ScienceTechnology-Society issues, Social Identity theory and new media mechanisms are analysed as key variables to understand what is new in today's science coming across citizenship. A discussion on the importance of accounting for the uncertainty inherent with the observations coming from crowdsourcing initiatives, possibly the most challenging side effect of what we call Citizen Science 2.0, closes the paper.
This paper, based on a social impact research and the possible NIMBY-effect of the Turin, Italy, co-incinerator, deals with risk perception, scientific literacy and their influence on the attitude towards high-tech and controversial industrial plants. The paper argues that plant and infrastructure settlements having a substantial ecological impact represent a highly sophisticated and diverse social phenomenon in which risk plays an important but not unique role. Taking into account some important concomitant variables (such as trust, mass media use, political culture in decision-making processes), it is first of all shown that risk is not a mono-dimensional concept, as assumed by the psychometric tradition, and that two dimensions of the concept are to be found. The collective dimension has a positive monotonic association with a critical attitude towards the co-incinerator, whereas the individual dimension has an unexpectedly negative correlation, which will be explained in further detail. It also demonstrates that scientific literacy has no statistical significance for attitude in our model, confirming the well-known limits of the so called 'knowledge deficit' model.
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