We live in a world where populist political projects are reviving the latent suspicion of many that elites, intellectuals, and their allies are not to be trusted. The public good, so the argument goes, can only be advanced by listening to ‘the people’ and at the same time by limiting the role of traditional elites and their academic allies. The inevitable response is that some of these same elites double down on the critical importance of science and evidence as the basis for government action. There are repeated calls for ‘evidence-based’ (or at least ‘evidence-informed’) policy as a partial offset to the populist and-intellectual surge.
But what does it take to actually move scientific knowledge into policy? While much has been written about this I recently published an article, with my friend and colleague Steven Hoffman, that seeks to bring to bear on this question some insights from the political science literature on the policy making process. We use the case of public health to argue that all too often efforts to do move evidence into policy rely on mechanistic and unrealistic views of the process by which public policy is made. As a result, traditional dyadic knowledge translation (KT) approaches may not be particularly effective when applied to public policy decision making. However, using examples drawn from public health policy, it is clear that work in political science on multiplicity, hierarchy and networks can offer some insight into what effective KT might look like for informing public policy. To be effective, KT approaches must be more appropriately tailored depending on the audience size, audience breadth, the policy context, and the dominant policy instrument.
You can read the full argument here.