While scalability and growth are key concerns for mainstream, venture-backed digital platforms, local and location-oriented collaborative economies are diverse in their approaches to evolving and achieving social change. Their aims and tactics differ when it comes to broadening their activities across contexts, spreading their concept, or seeking to make a bigger impact by promoting co-operation. This paper draws on three pairs of European, community-centred initiatives which reveal alternative views on scale, growth, and impact. We argue thatproliferation -- a concept that emphasises how something gets started and then travels in perhaps unexpected ways -- offers an alternative toscaling, which we understand as the use of digital networks in a monocultural way to capture an ever-growing number of participants. Considering proliferation is, thus, a way to reorient and enrich discussions on impact, ambitions, modes of organising, and the use of collaborative technologies. In illustrating how these aspects relate inprocesses of proliferation, we offer CSCW an alternative vision of technology use and development that can help us make sense of the impact of sharing and collaborative economies, and design socio-technical infrastructures to support their flourishing.

The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people “interested in watching something specific that they came to see” [6]. This can be found at the stadiums, amphitheaters, cinema, etc. In particular, we propose a novel dataset, the Spectators Hockey (S-HOCK), which deals with 4 hockey matches during an international tournament. In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc. The labeling focused on the game field also, permitting to relate what is going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard applications as people counting and head pose estimation but also for novel tasks as spectator categorization. For all of these we provide protocols and baseline results, encouraging further research.