Science-Policy Interaction

Science-policy interaction is understood here as “the ways in which research impacts on policy and policy draws on research”

Introduction

The Coastal Wiki contains a great deal of policy relevant information. Nevertheless, policy makers often complain about a lack of policy relevant research results and scientists often complain about the ignorance of policy makers of their policy relevant research results. Bridging the gap between policy and science is an issue which has triggered intensive debates over many years. No simple recipes have emerged. This article highlights some major causes of poor science-policy interaction and is intended as a help to avoid obvious pitfalls. It addresses in particular science-policy interaction related to environmental and societal issues.

Models of science-policy interaction

Fig. 1. The Knowledge Cycle: an idealistic conceptual model of Science-Policy Interaction.

The knowledge cycle depicted in the Fig. 1 provides an appealing model for science-policy interaction. The simplest interpretation of the picture is: science delivers facts and figures on which policy can build and policy formulates demands for lacking knowledge. However, reality is more complex, for several reasons.

Science is discipline-oriented
Most science funding is discipline-based, aimed at advancing knowledge at the frontiers of the current state of the art. Scientific recognition is based on publications in highly rated journals that are usually discipline-oriented. This research rarely meets the needs of policy makers. However, time spent on interdisciplinary collaboration to address real-world problems and on outreach activities to inform and engage the public and policy makers is not valued for a scientific career. Moreover, structural budgets for such activities are generally small or non-existent.

Scientific models do not represent the real world
The role of science is often seen as providing hard facts and figures. However, facts and figures produced by science generally refer to specific situations, both in time and geographically, which rarely correspond to those of policy interest. Situations of policy interest often lie in the future and are subject to more numerous interactions of greater complexity and to boundary conditions that are barely known. Policy-relevant results require extrapolation or generalization based on assumptions or models. But models are abstract intellectual constructs; they do not represent the complexity of the real world. The scientific input to policy is therefore fraught with uncertainty and arbitrariness, especially in situations where underlying (natural, social) processes are not properly understood. Science is an evolutionary (and sometimes even revolutionary) process, often with conflicting explanations for why things are the way they are.

Science-policy timescales do not match
Another reason for failure of the knowledge cycle are the different time scales at which science and policy progress: the knowledge cycle does not fit the policy cycle. Policy generally moves faster than science. Ongoing research produces new scientific evidence while policy decisions had to be taken already on the basis of earlier preliminary insight and forecasts. New theory, concepts, and empirical “facts” may emerge, pointing to opposite conclusions. This may frustrate the policy process and undermine the willingness of policymakers to listen to scientists and to invest in research.

Evolving evidence and insight
Policymakers and scientists have different perceptions of scientific knowledge. Policymakers want stable knowledge based on scientific consensus. For scientists, knowledge is work in progress that advances by questioning and debating controversial evidence and arguments. Science-based policymaking may even become an illusion in cases of strongly conflicting scientific opinions and frequently changing insight and forecasts.

Alternative models of science-policy interaction
Science-policy interaction according to the knowledge cycle encounters many obstacles if it is organized in the sequential way as depicted in Fig. 1. Several alternative organizations models of science policy interaction are schematically displayed in Fig. 2. These models can overcome various identified deficiencies of the sequential model. They involve

• Co-production: an iterative and collaborative process involving diverse types of expertise, knowledge and actors to produce context-specific knowledge and pathways towards sustainable policies.
• Knowledge brokerage: the full suite of activities required to link decision-makers with researchers, facilitating their interaction, to improve understanding of each other’s respective goals and professional cultures, to influence each other’s work, forge new partnerships and promote evidence-based decision-making.

Science-policy interaction is most effective and efficient in case of adaptive management, characterized by a structured, iterative process of optimal decision making in the face of uncertainty, through reviewing and adapting decisions as new information becomes available.

Fig. 2. Four alternative models of science-policy interaction. Red=Policy, Blue=Research, Green=Broker. After Cvitanovic et al. (2015).

Cvitanovic et al. (2015) list several measures to promote effective science-policy interaction:

• Research organizations should formally recognize and reward knowledge exchange activities. They should provide resources (funding, time) and promote professional development and training for scientists in knowledge exchange.
• Decision-making organizations should develop and communicate knowledge and information needs. They should provide staff with resources to access and interpret scientific knowledge (e.g., attend conferences) and provide opportunity for staff to participate in research programs.
• Funders/donors should establish new criteria for awarding research funds which include measures of stakeholder engagement. They should provide dedicated resources for knowledge-exchange activities (employment of knowledge broker) and fund the ongoing development of systematic reviews. These costs are usually underappreciated and underbudgeted, as knowledge exchange requires a lot of invisible work behind the scenes, for example, to develop relationships and clarify research questions.

Evaluation of measures to promote science-policy interaction is important to learn from successes and failures. There are few examples of documented evaluations of the outcomes of knowledge exchange processes. Evaluation is particularly challenging in relation to knowledge exchange activities, given that successful outcomes are difficult to define and thus measure. Similarly, the implementation of stakeholder surveys throughout the duration of a research project can provide information regarding the extent to which knowledge exchange has successfully occurred, and identify areas and options for future improvement.

Can policy relevant research be objective and value-free?

Frustration can arise from differing expectations about the weight of scientific evidence in policy decisions. Policymakers base their judgments not only on scientific evidence, but also take into account ethical values, cultural traditions, perceptions, feelings and emotions that live in society. This highlights the fact that sustainable coastal development cannot be achieved by "reason" alone. This also raises the question whether policy relevant research can be (or should be) objective and value-free.

Research results in the field of ICZM have societal implications when used by policymakers. Societal implications are not value free; they can be "good" or they can be "bad", according to different moral value judgements. Scientists are not robots; they are consciously or unconsciously guided by values, morals, ethics and norms and behavioral habits. What if scientific research yields socially undesirable outcomes? Can scientists remain indifferent to the societal implications of their research results? Should scientists privilege socially desirable outcomes? What if scientifically based insight is subordinated to opportunistic political choices? Should the scientific community actively engage in ways to bring about desired social change? For example, turning research on sustainability into research for sustainability? The science community is strongly divided about this dilemma. The normative stance entails the risk of losing scientific authority: science that is seen by policymakers as "just another opinion".

Scientists and their organizations have an ethical responsibility to make a more concerted effort to better engage with and communicate to end-users

Ten Ways of Conceiving the Research Policy Dynamic

Policy makers and scientists approach problems from viewpoints that are basically different. However, better understanding these different viewpoints contributes to filling the science-policy gap. Here we cite ten views of the problem of science-policy interaction, from the review paper Bridging Research and Policy by Diane Stone, Simon Maxwell and Michael Keating (2001):

1. The science-policy gap can be defined as a public goods problem, where there is an inadequate supply of policy relevant research.
2. The science-policy gap can be defined as one of a lack of access to research, data and analysis for both researchers and policy makers. Activities dedicated to improving both access to and the diffusion of knowledge should receive high priority.
3. The science-policy gap can be defined as the poor policy comprehension of researchers towards both the policy process and how research might be relevant to this process. Overcoming this lack of understanding requires researchers to study the policy process, to demonstrate the relevance of research, and to build methodologies for evaluating research relevance.
4. The science-policy gap can be represented as ineffective communication by researchers of their work. Improved communications strategies are consequently encouraged.
5. The science-policy gap can be defined as societal disconnection of both researchers and decision-makers from those who the research is about or intended for, to the extent that effective implementation is undermined. The appropriate focus is on (for example) ‘participatory analysis’, ‘street-level bureaucracy’ and encouraging ‘public understanding of science’.
6. The science-policy gap can be defined as the ignorance of politicians about the existence of policy relevant research, or the incapacity of over-stretched bureaucrats to absorb research. The solution – ‘building bridges’ or constructing ‘conveyor belts’ – takes form, for example, of conferences and workshops, or the appointment of specialists to government committees.
7. The science-policy gap can be conceived in terms of policy makers and leaders being dismissive, unresponsive or incapable of using research. This problem requires improvement in governmental capacity to recognize and absorb research, as well as in the capacities, personnel and resources of the state structure more generally.
8. The science-policy gap can be conceived of as not simply a question of research having a direct policy impact, but one of broader patterns of socio-political, economic and cultural influence. This leads to questioning of the domains of research relevance, impact and influence, and requires the adoption of a longer-term perspective where research may take a generation to exert real influence.
9. The science-policy gap can be defined as one of power relations. This generates concerns about the contested validity of knowledge(s), issues of censorship and control, and the question of ideology.
10. The science-policy gap can be viewed as one of the validity of research, and problems relating to the question: what is knowable? Attention is then focused on different epistemologies and ‘ways of knowing’.

A few guidelines for effective science-policy interaction

Roger Clark and Errol Meidinger (1998) propose in their review paper Integrating science and policy in natural resource management. Lessons and opportunities from North America several strategies for successful science-policy interaction.

The integration of new scientific information into policy is greatly facilitated for policies developed according to the principles of adaptive management. These principles emphasize uncertainty, the existence of multiple competing hypotheses, collective learning and incremental change. Adaptive management therefore can more easily cope with the continuing flow of new information produced by ongoing research. Adaptive management is also an appropriate strategy for learning what works and why, so that we can apply the lessons in the course of policy implementation.

Intermediaries between science and policy, individuals who can link the worlds of science and management and translate the concerns of one to members of the other, can be very helpful to streamline science-intensive policy processes. They are sometimes called “science brokers” or “boundary spanners”. Their efforts are generally aimed at evaluating, formulating, or altering management policy. They can also moderate cross-disciplinary working groups involving scientists and policymakers, to build a genuinely informed understanding of each other’s views and interests.

Clark and Meidinger mention several other important preconditions to successfully integrating science and policy:

• clarity of objectives, processes, and desired outcomes;
• clarity of roles and responsibilities of scientists, policymakers, and the public;
• quality control through open peer and public review;
• effective communication and involvement of stakeholders throughout the process.

The climate debate on the causes and impacts of global warming is an illustration of difficult science-policy interaction related to uncertainty and arbitrariness. The assessment process established by the Intergovernmental Panel on Climate Change provides an example of how to deal with this problem. Key characteristics of scientific international assessments, such as IPCC, are:

• they are demand driven, with involvement in the assessment process of the full range of decision-makers who would implement the potential responses;
• they are designed as an open, transparent, representative and legitimate process, with well-defined principles and procedures;
• they involve experts from all relevant stakeholder groups in the scoping, preparation, peer-review, and outreach/communication;
• the process incorporates institutional as well as local and indigenous knowledge whenever appropriate;
• results and analyses are technically accurate;
• conclusions are policy-relevant but not policy-prescriptive;
• conclusions are evidence-based and not value-laden, i.e. they are devoid of ideological concepts and value-systems, recognizing that the assessment conclusions will be used within in a range of different value-systems;
• they cover risk assessment and management;
• they present different points of view;
• they quantify, or at least qualify, the uncertainties involved.

However, the IPCC guidelines for avoiding normative statements are also criticized by members of the scientific community, who argue that critical knowledge about implementation gets omitted in the reports, ultimately slowing down progress towards identifying and learning about implementing solutions. In their view, research should focus less on understanding problems and more on how to effectively and efficiently steer and facilitate transformations towards mitigation and adaptation.

Science-Policy Interaction in the context of ICZM

Carapuco et al. (2021) make a distinction between 4 levels of science-policy interaction: outreach, crowdsourcing, management tools and co-production. The most effective way to contribute in the policy process regarding a particular coastal issue depends on the degree of public and political awareness and involvement. An important notion is that policies to which society does not (yet) adhere usually fail.

• Outreach – when public awareness is still low and the coastal issue is not yet on the policy agenda. Science contributions consist of popularized publications, exhibitions and media appearances targeting the general public by presenting scientific results in an understandable way to develop comprehension of both their meaning and implications.
• Crowdsourcing – when there is some public awareness, but involvement is still low. Initiatives aiming to involve the public in data collection and fact finding, using the practical experience linked with the day-to-day activities of coastal users. Besides enhancing coastal awareness, involvement in crowdsourcing creates a feeling of ownership of the coastal environment.
• Management tools – when there is political will to take action. Relevant information is made easily accessible and tools are developed to foster knowledge transfer and to make scientific results applicable in practice.
• Co-production – when there is high public and political awareness and strong involvement. A collaborative process in which policy-makers, coastal stakeholders and private actors bring a plurality of knowledge types together to address a particular issue, aiming at building an integrated solution. Co-production creates the possibility for coastal actors to share their knowledge and motivations and to shape consensual or best-compromise solutions around their needs. Co-production alters attitudes, fosters mutual trust and communication to shape consensual or best-compromise solutions.

When science-policy interaction is successful it not only increases scientific understanding on the coastal system but also fosters engagement and generates feedback. The feedback of a well-succeeded outreach initiative is raising awareness on coastal issues, thus contributing to trigger involvement of the audience and helps to change receivers’ attitude from passive to active, increasing their level of engagement.

Science-policy interaction is essential for the implementation of Integrated Coastal Zone Management (ICZM). The ICZM Recommendation of the European Union states, as one of the eight principles of good ICZM: “Adaptive management during a gradual process which will facilitate adjustment as problems and knowledge develop. This implies the need for a sound scientific basis concerning the evolution of the coastal zone.” The Evaluation report of the ICZM Recommendation stresses the need for improving the knowledge base for ICZM and recommends “to provide guidance and develop human capacities through education and training and to support ICZM training centers, staff-exchange opportunities, university courses and advanced adult education”. Capacity building for educating coastal managers is substantiated in several Coastal Wiki articles, e.g. Capacity Building Needs Associated to the ICZM Cycle, Consultation on Maritime Policy: the issue of Capacity Building and Problem structuring in decision-making processes. The "Ten Ways of Conceiving the Research Policy Dynamic" above also highlight the importance of policy awareness of researchers – an aspect that needs to be incorporated in Capacity Building programs. The development of Decision support tools is an important step in bridging science and policy, although it should be recognized that the use of these systems in decision-making processes is still limited. The Coastal Wiki is another major effort to disseminate scientific beyond the realm of specialists. The Main Page reminds authors that the Coastal Wiki is primarily meant for students, for transdisciplinary knowledge sharing and for disseminating knowledge to a broader audience than the circles of specialists working at the frontiers of science. Most articles are written from a science perspective, and only a small number of articles are authored by policy makers. This in a way illustrates the difficulty of establishing real science-policy interaction.

See also

External Links

• Conferences Bridging The Gap, https://www.kpsrl.org/event/bridging-the-gap-annual-conference-2019
• Clark, R.N and Meidinger, E.E. (lead authors) 1998. Integrating science and policy in natural resource management: lessons and opportunities from North America. Gen. Tech. Rep. PNW-GTR-441.Portland; U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. https://www.academia.edu/3151419/Integrating_science_and_policy_in_natural_resource_management_Lessons_and_opportunities_from_North_America?auto=download
• Court, J. and Young, J. 2003. Bridging Research and Policy: Insights from 50 Case Studies,

ODI Working Paper 213. https://web.worldbank.org/archive/website01031/WEB/IMAGES/ODI_BRID.PDF

Further reading

Bodo Von Bodungen and Kerry Turner, editors. Science and Integrated Coastal Management. Berlin: Dahlem University Press, 2001, 378 pp. ISBN 3 934504 02 7

The main author of this article is Job Dronkers Please note that others may also have edited the contents of this article. Citation: Job Dronkers (2023): Science-Policy Interaction. Available from http://www.coastalwiki.org/wiki/Science-Policy_Interaction [accessed on 1-11-2024] For other articles by this author see Category:Articles by Job Dronkers For an overview of contributions by this author see Special:Contributions/Dronkers J