Theme 3: Policy Impacts

This theme is coordinated by Jakob Edler from the Manchester Institute of Innovation Research (MIOIR) at the University of Manchester.

In this theme we seek to contribute to a better understanding of the impact of science on policy. While utilising the OSIRIS general framework, we develop a complementary, unique conceptual framework that will allow us to focus on the institutional conditions in the policy-making systems, as we are convinced that those conditions co-determine research agendas, patterns of co-production of knowledge, demand for and use of scientific knowledge, and thus its impact.

We will use our framework to conduct a number of case studies that are centred around established and new problem areas. All cases will be longitudinal, some of them historic process tracing, others ethnographic live cases. We will decide by late 2017 which areas we will tackle. A first short list explored in 2017 contains areas such as Artificial intelligence, Future of individual transport (regulation, liability etc.), Pollution of urban areas (re-recognition of this problem, remedies, causes), Migration (socio-economic research about causes and remedies) .Inequality (causes, consequences, remedies.

The theme will be closely integrated with the other themes, especially when it comes to overlaps with economic impact and impact on health care practice.

This framework and the empirical work based on it fills a gap in the vast existing literature on science impact on policy which has focused more on the science system itself, the perspective of scientists or the science–policy interaction.

Background and framework idea

The basic motivation for this theme emerges from four observations regarding impact of science[1] on policy making. First, despite a long history of looking at science – policy relationships and the use of scientific expertise and evidence for policy making there still seems to be a huge dissatisfaction with the way scientists and scientific results actually do inform policy making (Almeida and Báscolo, 2006; Smith, 2013, p. 4). Second, STI policies are increasingly formulated towards addressing global challenges, and funding systems are being re-shaped to support directionality of scientific knowledge production towards contributing to tackle challenges and serve missions. While science since the second world war has always had an element of mission orientation, the last decade, at least in Europe, has seen a broadening of this challenge and directionality approach in science funding, often framed in the language of crisis, response urgency and severity of the challenges to be tackled (Kuhlmann and Rip, 2014). In consequence, impact on policy of society more broadly, as one critical dimension in challenge orientation, has come to the fore again as a major justification of scientific activity. Third, and as a consequence of this trend, there is an increasing demand for the individual scientists to produce knowledge that has impact. Many Research councils (such as ESRC, NSF) now ask for explicit impact pathways and engagement strategies in funding applications, while in performance based funding systems, such as the UK REF, the explicit demonstration of impact ex post is becoming increasingly important for the assessment of organisations, and by implications, the scientists working within them. This puts the onus of generating impact fully on the scientists, as it is for them to choose topics and create engagement strategies that increase the likelihood of impact to occur. Fourth, in policy making, certainly in the UK, there is a strong revival of the idea that objective evidence can be produced on the basis of rigorous approaches, translated into layman language, and used by the policy making system co-determining decisions on policy (Parsons, 2002; Sanderson, 2009). In this reasoning, the more convincing the evidence, and the more convincing the translation into layman’s language, the more likely the scientific evidence has effect in the policy making process. Here, it is the nature of the evidence that determines the role it plays in policy making.

Against this background, there is a need for a change of perspective, to balance how we understand impact of science on policy. We think it is time to focus much more strongly on those non-scientific actors that co-determine research agendas, co-formulate the policy problem and absorb and utilise scientific knowledge in the policy making and implementation process – which has been found as being more important than the nature of the “product” (scientific knowledge) itself (Landry 1999).

The framework will follow a reflexive institutionalist approach, which assumes that while policy making is interest and power driven, the policy problems and normative and material interests are constantly redefined in the policy making process (Edler, 2003; Hall, 1993). Importantly, this definition process is influenced by the stock and flow of normative and cognitive ideas and their persuasive and legitimating power. Scientific knowledge is one important input in this (re-)construction of problem definition, interests and solutions, whereby scientists themselves do not occupy a neutral, objective position, but have their own – changeable - normative and material interests.

Within this theoretical understanding of the policy making process, our framework takes the qualities and processes of the policy making arena in the focus and consists of three pillars – which are interdependent:

(1) the four core institutional dimensions of the policy making arena and their meaning for the individual policy maker: Cognitive patterns, normative world views and basic paradigmatic positions, role perceptions, and Incentive structures;

(2) two mechanisms of mutual influence: funding and communicating

(3) the polity, politics and governance of the policy making process more broadly.

In a first step in this theme we will develop this conceptual framework and present at academic conferences and to broader stakeholders. It will then be used for a first pilot case study.

References

Almeida, C., Báscolo, E., 2006. Use of research results in policy decision-making, formulation, and implementation: a review of the literature. Cadernos de Saúde Pública 22, S7-S19.

Edler, J., 2003. How do economic ideas become relevant in RTD policy making? Lessons from a European case study. na.

Hall, P.A., 1993. Policy paradigms, social learning, and the state: the case of economic policymaking in Britain. Comparative politics, 275-296.

Kuhlmann, S., Rip, A., 2014. The challenge of addressing Grand Challenges: a think piece on how innovation can be driven towards the" Grand Challenges" as defined under the prospective European Union Framework Programme Horizon 2020.

Parsons, W., 2002. From muddling through to muddling up-evidence based policy making and the modernisation of British Government. Public Policy and Administration 17, 43-60.

Sanderson, I., 2009. Intelligent policy making for a complex world: pragmatism, evidence and learning. Political Studies 57, 699-719.

Smith, K., 2013. Beyond evidence based policy in public health: the interplay of ideas. Springer.

 

[1]     When we talk of scientists we include – in the continental European tradition – social scientists as well if not otherwise indicated.

Published May 13, 2017 11:39 AM - Last modified Nov. 8, 2019 12:45 PM