Make science systems more inclusive and equitable, facilitating a wider range of voices, institutions, types of knowledge and ways of learning Scientific knowledge is produced by disciplinary experts in dominant ways of knowing, rather than through more comprehensive or complex understandings. As a result, knowledge production and use often lacks systemic thinking and is dominated […]

Make science systems more inclusive and equitable, facilitating a wider range of voices, institutions, types of knowledge and ways of learning

Scientific knowledge is produced by disciplinary experts in dominant ways of knowing, rather than through more comprehensive or complex understandings. As a result, knowledge production and use often lacks systemic thinking and is dominated by linear and fragmented understandings of reality (Fazey et al., 2020).

Knowledge exists in many shapes and forms and comes from many different societal actors, yet the very use of the word science to refer to all scholarship tends to exclude marginalized perspectives, indigenous knowledge and stakeholders without scientific training. This slows the emergence of new thinking and acting, and furthers the disconnect between research and real-world issues.

Dominant scientific traditions must become more prepared to question their categories, languages and assumptions, and be more open to dialogue and collaboration with diverse knowledge cultures. Achieving a more cohesive society that is able to address complex challenges requires far greater efforts to acknowledge and include the many ways in which knowledge is produced. Science systems need to become much more collaborative and inclusive of different forms of knowledge.

Engage with indigenous knowledge

Indigenous knowledge builds on the long-term understanding and practice of socio-ecological systems maintained by societies around the world. Engagement with indigenous people, who have a diversity of know-how and cultures, for new collaborations along the chain of knowledge production is needed to co-produce informed policy, improved evidence and implementation of the 2030 Agenda. Indigenous knowledge on megatrends such as biodiversity, climate change adaptation and land conservation should be documented. However, strong respect and ethics are crucial throughout the process. Harnessing and securing indigenous knowledge must be undertaken with regards to intellectual property rights, which belong to indigenous people (UN GSDR, 2019).

Transcend science nationalism, particularly for issues of international importance

Currently research on the SDGs has a profile of around 80% domestic collaboration, 15% bilateral and 5% multilateral (Digital science, 2020). In the face of shared challenges, which are too complex and global in nature to be addressed by one country alone, there is a critical need to increase opportunities for international research collaboration.

Promote transdisciplinary research

For science to have a positive impact it needs to better connect with a range of societal actors, especially those who stand to be most impacted, and meaningfully integrate their needs and perspectives into the research process. Transdisciplinary research provides tools to achieve this. It not only stimulates closer cross-sectoral collaboration and better mutual understanding of divergent interests, but increases the ownership of results by different stakeholders. It also improves the ability to ask better scientific questions, and changes the paradigm of how scientists pursue science to include a solutions orientation. Through long-term partnerships, transdisciplinary research helps to create societal resilience to the shocks of “wicked” problems and contributes to increased support for science among the public at large.

Knowledge co-production, however, is not without costs, and these can represent a significant burden for participants. Novel methods are needed to help reduce costs and ensure effectiveness of and satisfaction with co-design and co-production. Overall, there is a need to become much savvier about how science partners with societal actors. The relations between societal actors and science will vary dramatically across issues, scales and contexts. A blanket approach promoting inclusion of all actors at the same time may be a recipe for stalemate. Therefore, a wider range of partnership possibilities should be considered, grounded in how these work in the real world rather than idealized prescriptions, for real progress in building partnerships between science and societal actors.

Facilitate long-term science-policy-society collaborations

Governments at every level should institutionalize science-policy-society alliances focused on co-designing, implementing and monitoring context-specific pathways to sustainable development. Actors from science, policy, the private sector and civil society must fundamentally rethink their partnerships and create experimental spaces for collaboration on transformation pathways – collaboration in which scientists and societal actors at different levels innovate sustainable solutions and develop, test and practice new routines in everyday life and business. Building effective collaboration with research capacities in the private sector will be critical to resolving many complex social and environmental problems.

Science-policy cooperation in particular needs to be intensified at the level of individual research projects and institutions to enable SDG implementation. This calls for a deeper understanding of science-policy interface processes. Strengthening the policy-science interface and enhancing policy coherence for sustainable development will also require a restructuring of national policy architectures. Engaging scientific representatives in national bodies for SDG implementation on a more formal and permanent basis is an important step towards strengthening the policy-science interface for the sustainability transformation.

Support interdisciplinary research through experimental and/or high-risk, high-reward projects

Such projects give researchers from different disciplinary backgrounds the opportunity to build long-term relationships and explore novel modes of collaborative research. The critical challenge is to understand how to facilitate effective and fruitful knowledge exchange between diverse disciplines, without diluting the capacity of each to effectively focus on their own part of the problem.

Stimulate a step-change in the role and support given to the arts, humanities and social sciences within inter- and transdisciplinary research

A primary focus should be on society: its mechanisms, dynamics, worldviews, values, and the instruments that help people shape people, rather than people shaping the biophysical world directly. In that respect, the inclusion of the arts, humanities and social sciences along with societal actors is paramount to maximize the relevance, legitimacy and uptake of research results that will lead to achievement of the SDGs. There is a need to better integrate insights, theory, knowledge on human behaviour, social relations, institutions and politics to inform and help shape society’s response to the SDG challenges. The importance of the arts, humanities and social sciences should not only be recognized at the individual and project level; there is also a need for a complete overhaul of research funding and evaluation systems at the national and international level to enable the meaningful integration of these domains into sustainability research from the outset, and not as an afterthought.

Furthermore, the share of research funding going towards the social sciences needs to increase substantially. For research on issues related to climate change, for example, the natural and technical sciences received 770% more funding than the social sciences between 1990 and 2018 (Overland and Sovacool, 2020). Such patterns are highly likely to be replicated in other areas of sustainable development research.

Mobilize and improve the use of existent knowledge

There are large bodies of academic, local and traditional knowledge that remain un-mobilized and under-used. The translation of existing knowledge into effective measures for change remains a critical gap. Science funding should not only support knowledge production, but also provide incentives for researchers to engage with policy and practice in order to promote the use of research evidence to inform choices and generate positive impacts. Implementation science also requires more attention within science systems as it explores methods and strategies to promote the uptake of effective interventions into practices, programmes and policies. Furthermore, large areas of existing knowledge should be systematically collected and synthesized in international and regional scientific assessments to build consensus on key issues and to explore new potential areas of application.

Redefine “excellence” and initiate new methods to evaluate research

Evaluation should break the grip of global rankings and leagues and the dominance of journal-level metrics like impact factor, in order to recognize, reward and value research that is engaged, solution-oriented and positioned for use. Funding systems need to move from an output paradigm to outcomes and impact evaluation. To really benefit sustainable development, a scientific publication should be the first step rather than the final product. Traditional science often stops with the publication because there is little funding available for other deliverables and/or translational science. Funding agencies should consider how to increasingly reward proposals that include deliverables beyond scientific publications and that take the scientific outcomes further.

Evaluation and review systems for funding research should also evolve to ensure that science is better connected with society. New evaluative criteria are required to support inter- and transdisciplinarity, including with the arts, humanities and social sciences. Similarly, new evaluative criteria are required to support contributions from indigenous knowledge and citizen science. These evaluation criteria should reflect social and environmental benefits of research, as well as the complexity and long-term challenges (rather than quick fixes) that substantive sustainability transformations imply. Effective ex ante and ex post evaluations should be conducted to assess the impact of research projects. For that, clearer guidelines should be developed for funding councils and universities to evaluate inter- and transdisciplinary research. Research Quality Plus is one example used to evaluate research for development.

Academic systems should also reward inter- and transdisciplinary co-creation of actionable knowledge and provide opportunities for long-term career development of inter- and transdisciplinary scholars, especially for early career scientists. Currently such opportunities are rare.

Establish transdisciplinary reference groups to assess research proposals

These reference groups include relevant disciplines for each project and also experts on transdisciplinarity who can help make the connections between the different approaches. Experts, including beyond academia, who have successful experiences in conducting transdisciplinary solution-oriented research could provide the leadership in reviewing proposals and manuscripts.

Shift from short-term project-based to long-term funding

Real progress and impact require consistent and long-term funding with an institutional and funder understanding that the outcomes may be “non-traditional”. Too much time is spent building research groups, creating fundamental trust and reciprocal working relationships, which are disbanded once funding stops. A longer-term funding system would enable research to build on existing partnerships. Therefore, long-term funding and support should be provided for research teams and institutions that are addressing wicked and complex problems. These teams should be drawn from various disciplines, including the arts, humanities and social sciences, and engage a range of relevant societal actors in the research process. To this end, research funding calls should have an explicit emphasis on social impacts of research and on inter- and transdisciplinarity, and should provide incentives for societal engagement and the promotion of evidence use, not simply its production. The development of these calls should also benefit from the input of the policy-makers whose responsibility it is to craft programmes that will use the knowledge that researchers create.

Prepare science systems to deal with future crises

The agility, quality and relevance of science need to be improved to deal with future crises. This can be supported by adapting funding regulations and mechanisms to deal with emergencies, promoting a systemic approach and providing incentives to redirect research. Publication needs to be sped up, both by ensuring quality control of pre-prints and incentivizing the publication of interim products of research. Mobilizing private sector science and technology platforms can also help science systems combat crises more effectively.

Build necessary capacities and skills

Being capable of working with complexity, values and diverse interests will be essential to dealing with contemporary wicked challenges. To this end, inter- and transdisciplinarity should be integrated into education and training of researchers at all levels (undergraduate, graduate and postdoctoral). Building science communication, facilitation and negotiation skills should also be considered as part of undergraduate and postgraduate studies. This will prepare scientists to act as knowledge brokers and to lead engagement with different stakeholders, bridging the divide between science, policy and society. Training in managing inter- and transdisciplinary research projects should also be offered. 

Tackling complex problems will require strengthened systems thinking capacities globally. There is also a need to develop a greater foresight and predictive capacity, especially on near-term timescales (daily to decadal) to make science more relevant to society. This would require both scalable community cyberinfrastructure to support near-real-time monitoring and forecasting, and education and training in predictive methods. Given the growing role of artificial intelligence and machine learning in science, data-driven analytical capacities of researchers would also need to be strengthened.

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