Abstract The geographical literature on communities of practice suggests that geographical proximity should not be confused with relational proximity, and that the latter is more important in determining how easily specialized knowledge can be jointly produced and shared through distributed innovation processes. However, the existing body of work has not specified the critical determinants of relational proximity, and the conditions under which we should expect it to be achieved effectively at a distance. This chapter reviews recent findings from a number of case studies in which distributed teams participating in joint problem-solving projects have attempted to engage in long-distance learning and knowledge translation, with varying degrees of success. Effective distanciated learning is shown to depend on the degree of social affinity between economic actors, and this affinity is comprised of several different dimensions: linguistic, educational, experiential, occupational, organizational, industrial, and institutional. The frictional effects of distance are also shown to depend on the types of knowledge supporting innovation in each case, with synthetic and symbolic forms of knowledge the least amenable to distanciated learning.
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