• 1. Anonymous backings were excluded from the analysis because it is impossible to identify how many of these are single people. Also, it is difficult to identify how many projects a single anonymous backer supported.
  • 2. Backers are not only individuals but also companies, organizations, collectives, and other forms of group representation. However, the nature of these supporters does not affect the present analysis, since I analyze the relationship between the projects and their creators and backers.
  • 3. Here, “centrality” denotes more ties (or relations) binding one node (backer or project) to the others, making them central points in the network.
  • 4. Because of data constraints, I decided to limit the argument on solitary support to cases with only one registered backing. support given to more than one project were divided into two categories—large and small backers—on the basis of the average support given in the absence of solitary support (2.77). This way, I attempted to control the effects of supporters who may be solitary or beginners in the field.
  • 5. The projects are included on the basis of their aggregate ranks as per solidary support and withdrawal from a project (column 2 in Table 17.1). Values were normalized on a scale of 0-100 using the formula [(X — min)/ (max — min) x 100]. Values close to 100 mean that projects’ attractiveness was more affected by social support, whereas values close to 0 imply that projects were more controlled by the field or submitted to rules for artistic style and other mechanisms.
  • 6. The number presented under the rankings denotes a project’s position in the network based on backers. For example, Zinecornio’s 239 rank in terms of solidary support includes not only projects, which were merely 80 within this number, but also additional backers who have more centrality (i.e. more ties binding them to other nodes of the network).
  • 7. Great supporters are defined by their rates of participation. Given the limitations of the given data, great supporters are defined as those who have an above average participation rate (2.77 or higher).
  • 8. The Pajek software did not complete the network to reach the limit of 18 because there were more than two actors with the same number of shared projects. The software, instead of randomly chosing the actors who would compose the network, chose to exclude them.


Bourdieu, Pierre. 1996. As Regras da Arte. Sao Paulo: Companhia das Letras.

-. 2006. O Poder Simbolico. Rio de Janeiro: Bertrand Brasil.

De Nooy, Wouter, Andrej Mrvar, and Vladimir Batagelj. 2011. Exploratory Siocial Network Analysis with Pajek, 2nd edn. Cambridge, UK: Cambridge University Press.

Granovetter, Mark S. 1973. The Strength of Weak Ties. American Journal of Sociology 78(6): 1360-1380.

Hanneman, Robert A., and Mark Riddle. 2005. Introduction to Social Network Methods. Riverside, CA: University of California, Riverside.

Lazega, Emmanuell. 2014. RedesSociaiseEstruturasRelacionais. Belo Horizonte: Fino Tra^o.

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