Science in practice ignorance and knowledge mobilization dynamics: PrioNet epistemic ecosystem laboratory case study (2005-2011)

How does a predominantly basic research university science laboratory adapt to membership in a primarily commercially-driven network? To address this question we propose a new concept – ignorance mobilization (IM) – within a dynamic model of ignorance and knowledge mobilization theoretical context. IM can be understood as the use (or consideration) of ignorance towards achieving social, cultural, political, professional, and or economic goals. If ignorance is a key indicator of a knowledge society (Gross 2010; Smithson 2009; Kasperson 2009), gaining greater understanding of its dynamics is relevant not only for science policy but more fundamentally for science in practice – understanding the probationary and dynamic nature of scientific knowledge and ignorance and related epistemic and social processes.

The knowledge mobilization (KM) approach of investigating processes in knowledge generation, transmission, reception, evaluation, and management has proven insightful in investigating scientific activity. Focusing on the case study of a university laboratory in a scientific network (PrioNet Canada) we explore a potential KM approach blind spot, however, IM.  IM is reminiscent of 'usable ignorance' (Ravetz 1987). Though integral to scientific practice and a subtext in scientific policy, ignorance generally remains ill-accounted for theoretically and analytically (Bammer and Smithson 2009; Gross 2010; Schneider 1962). In this regard KM falters by inadequately addressing the increasingly interdependent and complex relationship between ignorance and knowledge production especially where production and application converge. This is particularly relevant for networks where commercialization and commercial capital production are valued knowledge application processes.

The case study is an application of the proposed theoretical context where we argue that, from 2005 to 2011, the dynamic calibration and re-calibration of IM and KM appears to have been one of the main laboratory adaptation processes. Analysis draws on scientometric methodology with temporally-sensitive social network analysis, semi-structured interviews and scientific publication discourse analysis.