Knowledge and place-based development – towards networks of deep learning

The influential work by Barca on place-based development, which has permeated policy and academic discourses alike in recent years, builds on the premise that localities are expected to utilize their endogenous potential rather than placing their trust in redistributive policies. This endogenous potential involves local knowledge and place-based knowledge, and how these two types can tap into actions. This has barely been explored in a systematic and comparative manner. This paper therefore examines 20 urban and rural development actions across Europe in order to understand how, and the extent to which, local knowledge and place-based knowledge are mobilized (or not). It makes use of empirically informed evidence to identify evolving mechanisms and to analyse how learning loops are triggered. We argue that it is crucial for leading actors in such development actions to pay attention to these different mechanisms of mobilizing these two types of knowledge and how to trigger learning loops. Since this analysis also highlights a number of shortcomings and inhibitors regarding the extent to which these collective knowledge and learning capacities actually inform actions over time, the concept of ‘networks of deep learning’ is suggested as a knowledge management principle for key actors in local governance.