1. Workshop Policy & Science: Who defines the problem? 7 th of July 2014, Charles Darwin House, Central London Workshop Policy & Science: Who defines the problem? 7 th of July 2014, Charles Darwin House, Central London Dialogue with Policy Martina Padmanabhan Universität Passau Chair of Comparative Development and Cultural Studies SEA

3. 1. Science policy-interface: Science myth Complex systems can be fully understood Uncertainty is reducible Simple cause-effect relationships can always be established

4. 2. Science policy-interface: Policy myth Social-ecological systems must be understood before deciding With enough knowledge these systems are controlable A decision is the end of a linear process, neutrally considering pros and cons

5. 3. Science policy-interface: Science-policy myth Science and policy are two independent domains Truth speaks to power Forums, where reported results lead to policies based on evidence

6. 6 Policy-science interfaces: …are the many ways in which scientists, policy makers and others link up to communicate, exchange ideas, and jointly develop knowledge to enrich policy and decisions-making processes and research. …are complex interaction and learning processes.

7. Science policy-interface 1. Key feature: Goals Clarity of scope and transparency of vision Objectives Drivers like mandates or demand from policy or supply by research

8. Science policy-interface 2. Key feature: Structure Independence of the science-policy interface: control and biases Range of interest, expertise and openness Financial and human resources

9. Science policy-interface 3. Key feature: Processes Trust building Building capacities by making scientists understand policy makers and vice versa Adaptability Procedures to anticipate developments Continuity of iterative process Conflict management

10. Science policy-interface 4. Key feature: Outputs Relevance of timely and accessible i.e. policy briefs Quality ensurance Convey message across different domains relevant for various audiences

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13. Science policy-interface 5. Key feature: Outcomes Social learning and change of thinking Behavioural impacts Policy impact Issue impact

14. Attributes of sucessful interfaces To understand influence and impact Evaluate scenarios Draw lessons from past experiences Explain assessments‘ influence 1.Credibility 2.Relevance 3.Legitimacy 4.Interation

15. Achieving Credibility Credibility is the preceived quality, validity and scientific adequacy of the people, processes and knowledge exchanges at the interface  Interface as seen by others  Role of strategic „champions“ and charismatic „ambassadors“  Transparency and traceability

16. Enhancing Relevance Relevance is the perception of the usefulness of the knowledge brokered, how well it relates to the needs of policy and society and how responsive the interface process is to the changing needs  Continous policy support builds trust  Communicating understandably at relevant events  Using „translators“ and „knowledge brokers“

17. Building Legitimacy Legitimacy is the perceived fairness and balance of the interface process  Important when knowledge is contested and decisions produce losers and winners  Wide participation of different groups: Multi-stakeholder dialogue  Conflict management

18. Dynamic Iteration Iteration is the dynamic interaction between science and policy  Emphasis on added value of dynamic and repetitive feature of interfaces  Important to consider long-term develoment  Knowledge accumulates to institutional memory

19. Pitfalls of science-policy interfaces 1.Unclear goals and functions of interfaces 2.Power influences lead to conflicts 3.Interaction with media perceived as risky 4.Focus on key individuals risky 5.Lack of resources: interface as marginal activity

20. What to do? Designing interface even before inception - conceptualising interface ex ante Monitoring interface work – reflect on learning process Improving communication - what role may art play in this?

21. 21 Changing the institutional landscape Draw conclusions for research policy by: enabling a learning culture in research Co-design, co-creation, co-evaluation

22. Thank you for your kind attention! 22

23. Sources  Information on research group BioDIVA http://www.uni-passau.de/en/biodiva/home/  A website on the Net-Map toolbox for influence mapping of social networks (as developed by Eva Schiffer) http://netmap.wordpress.com/http://netmap.wordpress.com/  The Spiral project on ‘Interfacing Biodiversity and Policy’ http://www.spiral-project.eu/http://www.spiral-project.eu/  Information on the Project PoNa  Shaping Nature: Policy, Politics and Polity http://www.sozial- oekologische-forschung.org/en/1427.phphttp://www.sozial- oekologische-forschung.org/en/1427.php 23