Results from World Café exercise | Online Workshop from STRINGS project

On October 29^th, we held a virtual workshop to outline, in a collaborative way, what type of scientific research is most helpful in addressing Chagas disease, which is one of the case studies carried out in the context of the STRINGS project. Fifteen people participated from different parts of the country. They contributed their experience and perspective on the topic. Among participants, there were actors from scientific, public policy and civil society organisations.

The workshop was organized with a “World Café” methodology: three discussion tables were proposed, around which all participants rotated over two and a half hours. The discussions were very rich; they were carried out in small groups and we noticed interest in participating and a fair word circulation. We synthetise the main points that emerged in each table below.

Table 1: Why are some topics more highly prioritised in the scientific agenda about Chagas?

Participants contributed with different views. They pointed to funding schemes as one of the main reasons for the prevalence of certain research topics over others. This generates a bias: as there is more research on certain topics, there are also more academic outputs (publications/patents) and experience, which further reinforced the existing research trajectories. In addition, funding schemes often depend heavily on political-regional strategies affected by different interests. Some participants argued that the research system is tied to an economic system that privileges research in areas in which greater future economic returns can be obtained.

In turn, scientific institutions developed a trajectory on certain topics and it is then difficult to change the course. There are sunk costs associated with starting new research lines. The importance of the incentive system to guide research was also mentioned. In particular, it was pointed out that interdisciplinary research is not promoted since its potential for social impact has only recently been acknowledged

There were some claims for scientific policy to be more explicit about directions to be promoted. In particular, many agreed that there is a need to better articulate the scientific research on Chagas within the public policies arena and the health system.

Several participants highlighted that science and technology policies do not take into consideration the multidimensionality of Chagas: for example, the programme “Argentina Innovadora 2020” only funds the development of diagnosis kits and vaccines. Some participants claimed that they were concerned that the idea that Chagas should be addressed “more at the laboratory than in the territory” is extending. They argued that this conviction only contributed to further consolidating the biomedical hegemony.

In addition, we found that there are still concepts under dispute: while some argued that Chagas is a disease that should be tackled both from the medical and social perspectives,  others pointed out that it should not be thought of as a disease but rather as a complex and multidimensional problem.

Table 2: What are the social needs for scientific production in relation to Chagas?

Carlos Chagas´ studies identified the parasite, the vector and a series of clinical manifestations of the disease more than one hundred years ago. Yet, today Chagas continues to be a very important problem, with multiple dimensions to address. What knowledge is lacking to better address this complexity? For various participants, the question about “knowledge” referred to contributions from biology or medicine, where most of the scientific production on Chagas is concentrated. For many, “knowledge” is not lacking, but instead, there is a need for knowing better how to apply scientific knowledge.

Enhancing the articulation between the scientific systems and those who make decisions was seen as a strategy to address such shortcomings. The need to link the State with the companies to develop new technologies was also noted. Problems of communication and education were highlighted as well: “We need a health system that sees Chagas as an existing problem”.

Several participants commented on the need to improve the approach on public policy to be more comprehensive, taking into account biomedical, epidemiological, economic, political and cultural issues. Among other aspects, some participants mentioned the need to empower affected communities to defend their rights, arguing that in relation to other problems with lower incidence of affected people, such as HIV, the scientific and political solutions that emerged were triggered by pressures from an organized community.

Table 3: How can Open Science help to produce knowledge better aligned with needs?

The definition of “Open Science” was presented at the beginning of the session as research done in collaboration with diverse academic and non-academic actors including potential users. Open science practices promote sharing openly both research outputs and processes. In addition, they include extensive engagement, outreach and communication activities to enhance science-society connections. “Open Science” was presented as opposed to “Conventional Science”, defined as professional scientific research based on expert knowledge carried out in academic spaces or laboratories. The main aim is to design technical solutions that can be transferred to society using different policy schemes such as public-private research partnerships, or technological licenses or contracts of technical assistance.

The participants agreed that the definition of Open Science is very broad and that, depending on how it is interpreted the contribution may vary. Thus, there were discussions about different strategies related to, on the one hand, sharing scientific resources openly and on the other, enhancing collaboration, especially with social actors who may participate in knowledge production processes. Open access databases were seen as valuable for drug discovery and development. Similarly, the possibility of sharing data from clinical records was seen as important to move ahead in developing better diagnosis methods and treatments. Most participants agree that sharing knowledge saves time and resources. It was suggested that science policy should promote these types of strategies, especially for research funded by public sources.

There was a fairly wide agreement around the idea that interaction among different actors is enriching. In addition, inter- and transdisciplinary research, which also includes the affected population, was seen as superior to address complexity. It was claimed that in this way science could be better situated, and therefore, it may be in a better position to respond to societal needs. Participants also mentioned the need to translate research results for a wider and diverse audience. However, many participants proposed that these activities should be carried out by people trained in communication skills, who should be involved in research teams. This was seen as important not just to avoid putting too much pressure on researchers’ time but especially because they may not be the most capable to pursue those tasks effectively. In addition, the use of social networks was seen by some participants as an effective channel to interact and exchange with other actors. “These networks create a back and forth channel with the people, and when the laboratory opens the projects are enriched”. Finally, open science was also seen as enabling the possibility of involving social actors in the design of a research project, which may further contribute to translating scientific language into plain language.

This post was originally published on Leiden Madtrics, the official blog of STRINGS partner the Centre for Science and Technology Studies (CWTS) at Leiden University.

For research to address societal challenges, indicators of average degree of ‘interdisciplinarity’ are not relevant. Instead, we propose a portfolio approach to analyze knowledge integration as a systemic process; in particular, the directions, diversity and synergies of research trajectories.

‘Convergence’ as knowledge integration for grappling with societal challenges

Last October the US National Academies held a workshop (available here) to gather views on how to better measure and assess the implications of interdisciplinarity, or convergence, for research and innovation. The use of the term convergence as a synonym of interdisciplinarity followed from two previous reports by the National Academies (2014 and 2019). These reports understood convergence as the ‘integration of knowledge and ways of thinking to tackle complex challenges and achieve new and innovative solutions that could not otherwise be obtained.’ (A discourse that echoes European discourse on interdisciplinarity for grand challenges and missions.)

In this blog, I will summarise the argument I put forward in the workshop: that for mapping progress towards this goal (that is: the successful knowledge integration for addressing a given societal challenge), we should conduct multidimensional portfolio analyses on the types of knowledge to be integrated rather than produce synthetic indicators of interdisciplinarity.

For two main reasons. First, since knowledge integration for societal challenges is a systemic and dynamic process, we need broad and plural perspectives and therefore we should use a battery of analytical tools, as developed for example in research portfolio analysis, rather than a narrow focus on interdisciplinarity. The second reason is that while interdisciplinarity is one (but not the only) of the relevant concepts in knowledge integration, the concept of interdisciplinarity is too ambiguous, diverse and contextual to be captured by traditional indicators, as discussed in a previous blog.

Fostering plural innovation pathways in the face of uncertainty and ambiguity

It has long been argued that addressing societal challenges, such as climate change or COVID-19, benefits from the combination of disparate types of knowledge. Societal challenges are ‘wicked’ problems, in the sense that the framings of both the problems and the solutions are complex, disputed and uncertain.

Under these conditions of ambiguity and uncertainty, research contributions are likely to come from combinations of diverse types of knowledge (or ways of knowing). This is: diversity within projects is needed. However, diversity across projects is also necessary. Since we do not know or even agree in advance on what types of expertise are appropriate to tackle a given problem, it is also important to have a plurality of research trajectories. Take the example of malaria: in spite of decades of efforts to develop drugs or vaccines, the most successful strategies so far have been fighting mosquitoes that transmit it, in particular with insecticide-treated bed nets.

Therefore, rather than just aiming at fostering a ‘melting pot’ of disciplines, research systems should also produce a high number of disparate research trajectories – knowing that only some of them will ever be technically successful.

Moreover, different research and innovation pathways are not equally desirable from a public value perspective – directionality matters. Some solutions are more socially preferable than others depending on their effects on public goods such as equity or environmental sustainability. Which means that public investment, while keeping a diverse portfolio of research strategies, should favour those which are perceived as more socially robust and relatively underfunded by the private sector.

In summary, policy for science and technology (S&T) convergence should aim at fostering systemic diversity, rather than interdisciplinarity in every single project or program, but it should also take into account the preferred research directions in particular contexts or societies.

Figure 1. Comparison of the focus of rice research in India and the US (2000-2012). Red areas indicate areas of high density of publications. From Ciarli and Rafols (2019).

From ‘measuring’ interdisciplinarity to multi-level mapping of knowledge integration

Measurement approaches to convergence should reflect this turn towards a systemic perspective on knowledge integration for societal challenges.

This shift in the conceptualisation of S&T indicators from individual to systemic properties is similar to the shift in biology towards ecological approaches. The forest should not be measured by the average size of its trees or the timber it yields (scalars), but by the distribution (vectors) of all types of species and how they interact (matrices). Because the wealth, in sustainable terms, that can be derived from the forest comes from this diversity: water resources, herbs and mushrooms that unexpectedly yield nutritional or pharmacological benefits, spaces for leisure and well-being, etcetera.

Similarly, the ‘solutions’ to societal challenges will not emanate from 1,000 labs with the same combination of disciplines, but from labs of various epistemic combinations and social embeddings. Therefore, our measurement should not focus on an average degree of interdisciplinarity. Instead, it should focus on mapping the directions and diversity of research approaches. To do this, we need to shift towards statistical descriptions of the vectors and distributions of research trajectories over knowledge landscapes. A framing in terms of research portfolios can help conduct this type of analyses.

Portfolio analysis: exploring directions, diversity and synergies

In a nutshell, the key idea is that for a given societal issue, the contribution of research should be explored by mapping the relevant types of knowledge over a research landscape (e.g. see obesity). The portfolio or repertoire of a given laboratory, university or territory, can then be visualised by projecting (overlaying) their activities of this research landscape, as illustrated in the figure above for ‘rice research’ (or avian flu).

First, this portfolio provides us with information on the main directions that the research on a given topic is taking – which is pointing to the type of solutions envisaged for a grand challenge. For example, in the example in the figure above on rice, if the focus is related to genomics, mainstream research investments can be expected to deliver via Genetically Modified seeds (the case of the US). But if the focus is in fertilizers and yields (the case of India), the main goal is to increase productivity.

Second, the portfolio can tell us about the diversity of research efforts, i.e. whether investments are heavily concentrated in a few areas, or distributed across a variety of fields. In the face of uncertainty and contested views on preferred innovation pathways (e.g. in renewable energies), one would expect a variety of pathways to be supported. This way the bets are hedged against unexpected scientific results or social reactions to certain approaches. Indicators of interdisciplinarity provide a view of the epistemic diversity in specific projects, labs or centres. This is a valuable but only a partial perspective of the research landscape.

Third, by analysing the interrelations between innovation areas, a portfolio approach helps think about the synergies or lack thereof across research pathways. For example, in a portfolio of energy technologies, solar cells and small wind turbines have positive synergies as they both fit with distributed electricity infrastructure, while they have negative synergy with nuclear energy which needs centralisation. Understanding these positive or negative relations is important in balancing portfolios.

From ‘atomistic’ to systemic and dynamic descriptions

In summary, since social contributions are multifaceted, the analysis of research for societal challenges needs to adopt systemic perspectives, and thus take multidimensional forms. Research portfolio analysis offers a battery of tools, among other possibilities of exploring systemic properties of a research landscape. While interdisciplinary research is paramount in certain points, it is not required across the whole landscape. Therefore, rather than indicators of aggregates or averages, we need rich description of knowledge landscapes including the directions, diversity and synergies of research trajectories.

The 2030 Agenda for Sustainable Development and its Sustainable Development Goals (SDGs) acknowledge that science, research, technology and innovation (STI) are vital drivers of the global transformation towards a better and more sustainable future for all.

However, the impact of STI investments and policies on the SDGs is complex, often intangible and full of synergies and trade-offs.

As part of STRINGS’ work to better understand these complex relationships, we set out to analyse the main findings from publications (both scientific papers and grey literature) that examine the relationship between STI and the SDGs. This blog summarises the themes emerging from this literature review, and the implications for efforts to better align STI with the SDGs.

After developing a search methodology and selecting the most relevant literature produced between January 2014 and September 2020, we grouped the publications in four broad, related themes:

1. Misalignment between STI investments and the SDGs.
2. Approaches to shaping STI towards the SDGs.
3. Synergies and trade-offs between SDGs.
4. Monitoring of the success of STI for the SDGs.

Misalignment between STI investments and the SDGs

Publications in the first theme consider the reasons for the potential misalignments between STI investments and the SDGs.

One issue highlighted is the uneven distribution of STI activities across countries, which biases the focus of STI endeavours to thematic areas and societal problems unrelated to the problems of the worldwide majority^1–4. For example, high-income countries perform most of their medical research on diseases (e.g. cancer) that are not the ones with a higher global disease burden (e.g. infectious diseases).

Another factor mentioned is that societal priorities differ substantially with economic status within countries. For example, according to a survey sent to 34 African countries⁵, hunger (SDG 2), health (SDG 3), water and sanitation (SDG 6), access to energy (SDG 7), and infrastructure (SDG 9) matter more to the poor. In contrast, the wealthiest respondents were more likely to cite jobs and economic growth (SDG 8) peace, justice and strong institutions (SDG 16) as priorities.

Since, in most countries, STI priorities emerge from complex interactions between policymakers, funders, researchers and innovators, each with their incentives and institutionalised practices, it is possible that in many cases STI prioritisation is not well aligned with the needs of the poorest.

Another important factor identified is that some forms of contemporary STI also contribute to environmental degradation, disruption of livelihoods and exacerbate inequalities⁶. UNDP (2018), for example, argue that at least nine SDGs could clearly be negatively impacted by advances in automation and artificial intelligence, primarily through the direct and indirect consequence of increased unemployment but also through threats in emergent sectors like the “gig” and “on-demand” economies.

Approaches to shaping STI towards the SDGs

The second theme identifies various approaches that can be taken to shaping STI towards the SDGs. They include:

1. Directionality of STI policies towards the SDGs. This may take the form of challenge or mission-oriented approaches, or other incentives for directing STI activities towards the SDGs.^6,8–10
2. Plans, roadmaps or integrated assessments of STI investments and policy which are agreed by public, private and civil society actors.^11–17 For example, identifying technology gaps or creating research and development roadmaps.
3. Promoting inclusive and grass-roots innovation policies that consider the specific situations and needs of poor people, women and vulnerable groups to achieve more equitable, sustainable and inclusive development.^6,18
4. Strengthening national systems of innovation in developing countries (e.g. improving infrastructure, lowering barriers to technology deployment and diffusion, building STI literacy and capabilities, strengthening the science-policy interface) and fostering well-functioning institutions (e.g. strengthening political stability, educating workforces, strengthening the science-policy interface) in order to reinforce the economic, environmental, social and cultural resilience within societies that will contribute to the achievement of the SDGs.^1,18–21
5. Using the SDGs as an opportunity for developing countries to “leapfrog” to sustainable frontier technologies²². For example, some people in developing countries that have had no electricity until now are bypassing fossil fuels by adopting solar electricity and leaping directly to the stage of renewables. By doing this, they are not only contributing to the realisation of SDG 7, but also developing capabilities and skills in a set of technologies that will be critical in the future.
6. Finally, another framework looks at the transformations/transitions^23–25 required in the wider economy to achieve the SDGs by 2030 (e.g. where technology ownership and control lies, its existing orientation and focus, etc.).

Synergies and trade-offs between SDGs

The third theme relates to the synergies and trade-offs between SDGs. It is argued that studying the interaction between SDGs is essential for the efficient design of public policies, since an integrated approach can save resources and reduce costs by exploiting the positive interlinkages (“synergies”) and minimising the negative ones (“trade-offs”)^26–33.

The literature has applied various methodologies to study the linkages between SDGs, although most analysis assesses these interactions at the target level. On the empirical side, many authors have used time series of SDG indicators to correlate progress between them^34–36.

Other approaches have relied on expert opinion, theoretical models or a review of the literature to identify essential interlinkages^37–41. Additionally, text mining approaches have proven to be a successful methodology used in the literature to assess synergies and trade-offs^42,43. For instance, Le Blanc (2015) finds that from 107 targets, 60 explicitly refer to at least one other goal than the one to which they belong. This aspect of the SDGs is frequently mentioned as an improvement on the Millennium Development Goals, which did not form such an integrated system⁴⁴.

Overall, there is an agreement on the fact that positive interactions between SDG targets outweigh the negative ones^36–38,45, however, there is also consensus that the interactions between SDGs are greatly context-dependent. Namely, that the relationships between different SDG targets can depend on the geographical locations, governance context, number and types of people affected, and its time frame^27,37,46.

For example, increasing fishing activity in a certain region can lead to a reduction of hunger and improved livelihoods in the short-term. With time, however, fish stocks may be at risk of becoming overused with the same effort leading to less and less yield unless sustainable management practices are put in place. The context-dependencies listed previously often make it difficult to draw generalisable conclusions about interactions that may ultimately depend on locally specific factors³⁸.

Monitoring the success of STI for the SDGs

The inherent complexity of all 17 SDGs and the variety of pathways by which different areas of STI can contribute to the achievement of specific targets makes it very difficult to rigorously evaluate progress and impact^15,47,48. Yet, the existence of indicators aligned with the SDGs targets and rules for the collection of standardised data open an important opportunity for the monitoring and control of the relations between STI and the SDGs^49–51.

An important issue relating to SDG indicators is that many national statistical systems have faced severe challenges in tracking progress, which requires an unprecedented amount of data and statistics at all levels⁵².

An analysis of the indicators in the Global SDG Indicators Database⁵³ reveals that for four of the 17 goals, less than half of the 194 countries or areas have internationally comparable data. Even countries with available data have only a small number of observations over time, making it difficult for policymakers to monitor progress and identify trends.

Therefore, increased investments in national data and statistical systems and the mobilisation of additional international and domestic resources are needed to maintain adequate coverage of all population groups, as well as to guarantee the internal consistency, comparability and overall quality of data produced to advance implementation of the 2030 Agenda.

This is especially relevant in lower-income contexts, where these actions and investments should be complemented by an operational/technical assistance budget dedicated to monitoring and evaluating policy. It is argued that, in these contexts, enhancing capacities related to monitoring and accountability seems to be essential to set up policies that contribute to achieving the SDGs ^13,54,55.

Finally, on a positive note, it has been argued that advances in technology and the proliferation of data are providing new opportunities for monitoring and tracking the progress of the SDGs. A promising avenue is the data produced through citizen science, which can complement and ultimately improve the SDG reporting process^56,57. In this vein, Fritz et al. (2019), demonstrate the value of using data from citizen science for the SDGs, and provide concrete examples of how such data are currently being adopted and potential areas for future contributions. For example, volunteers in the Philippines are collecting household census data on poverty, nutrition, health, education, housing and disaster risk reduction, which are used by the Philippine Statistics Authority to enhance their statistics on 32 SDG indicators.

Conclusion

In summary, this literature review found many publications proposing approaches to helping shape STI investments and policies towards the SDGs. Yet, we found that less effort has been made in trying to understand what works and how to evaluate the efficacy of such approaches.

This gap is one of the things we are working on in STRINGS. By developing methodologies that help track misalignments between STI and the SDGs at the global level (for example, using bibliometric and SDG indicator data) and by analysing how policies are working, or not, to achieve the SDGs in our case studies in East Africa, India and Latin America, we are seeking to address this important question.

References

1. United Nations. Science, technology and innovation for the post-2015 development agenda: Report of the Secretary-General. (2014).
2. Walsh, P. P., Murphy, E. & Horan, D. The role of science, technology and innovation in the UN 2030 agenda. Technol. Forecast. Soc. Change 154, 119957 (2020).
3. United Nations. The future is now – Science for achieving sustainable development. Global Sustainable Development Report 2019 213, (2019).
4. Yegros-Yegros, A., van de Klippe, W., Abad-Garcia, M. F. & Rafols, I. Exploring why global health needs are unmet by research efforts: the potential influences of geography, industry and publication incentives. Heal. Res. Policy Syst. 18, 47 (2020).
5. Coulibaly, B. M., Silwé, K. S. & Logan, C. Taking stock Citizen priorities and assessments three years into the SDGs. 0–34 (2018).
6. UNCTAD. New Innovation Approaches To Support the Implementation of. (2017).
7. UNDP. Development 4.0: Opportunities and Challenges for Accelerating Progress towards the Sustainable Development Goals in Asia and the Pacific. (2018).
8. Giovannini, E., Niestroy, I., Nilsson, M., Roure, F. & Spanos, M. The Role of Science , Technology and Innovation Policies to Foster the Implementation of the Sustainable Development Goals (SDGs). Report of the Expert Group “Follow-up to Rio+20, notably the SDGs”. (2015). doi:10.2777/245398
9. Mazzucato, M. Mission-Oriented Research & Innovation in the European Union: A problem-solving approach to fuel innovation-led growth. (2018). doi:10.2777/36546
10. UNCTAD. Effectively harnessing science, technology, and innovation to achieve the Sustainable Development Goals. 06339, (2018).
11. Miedzinski, M., Mazzucato, M. & Ekins, P. A framework for mission-oriented innovation policy roadmapping for the SDGs : The case of plastic-free oceans. (2019).
12. IATT. Guidebook for the Preparation of Science, Technology and Innovation (STI) for SDGs Roadmaps. (2020). doi:10.1017/CBO9781107415324.004
13. IAP Workgroup. Improving Scientific Input to Global Policymaking: with a focus on the UN SustainableDevelopment Goals. (2019).
14. IATT. Science, Technology and Innovation for SDGs Roadmaps. Technol. Facil. Mech. 1, 8–57 (2018).
15. United Nations. Science, technology and innovation for sustainable development. 00233, (2016).
16. Allen, C., Metternicht, G. & Wiedmann, T. Prioritising SDG targets: assessing baselines, gaps and interlinkages. Sustain. Sci. 14, 421–438 (2019).
17. United Nations Economic and Social Council. Strategic foresight for the post-2015 development agenda. E/CN.16/20, 1–19 (2015).
18. United Nations. Indigenous and Local Knowledge(s) and Science(s) for Sustainable Development. (2016).
19. United Nations. Perspectives of Scientists on Technology and The SDGs. in Global Sustainable Development Report 2016 2030, 41–60 (2016).
20. IAP. Harnessing Science , Engineering and Medicine ( SEM ) to Address Africa ’ s Challenges : (2019).
21. Leal Filho, W. et al. Reinvigorating the sustainable development research agenda: the role of the sustainable development goals (SDG). Int. J. Sustain. Dev. World Ecol. 25, 131–142 (2018).
22. United Nations. World Economic and Social Survey 2018: Frontier technologies for sustainable development. E/2018/50, (2018).
23. Sachs, J. D. et al. Six Transformations to achieve the Sustainable Development Goals. Nat. Sustain. 2, 805–814 (2019).
24. Schot, J. & Steinmueller, W. E. Three frames for innovation policy: R&D, systems of innovation and transformative change. Res. Policy 47, 1554–1567 (2018).
25. TWI2050 – The World in 2050. Transformations to Achieve the Sustainable Development Goals. Report prepared by The World in 2050 initiative (International Institute for Applied Systems Analysis (IIASA), 2018). doi:10.22022/TNT/07-2018.15347
26. Alcamo, J., Grundy, C. & Scharlemann, J. Interactions among the sustainable development goals, and why they are important. (2018).
27. Scharlemann, J. P. . et al. Global Goals Mapping: The Environment-Human Landscape. A Contrib. Towar. NERC, Rockefeller Found. ESRC Initiat. Towar. a Sustain. Earth Environ. Syst. UN Glob. Goals 150 (2016).
28. Allen, C., Metternicht, G. & Wiedmann, T. Initial progress in implementing the Sustainable Development Goals (SDGs): a review of evidence from countries. Sustain. Sci. 13, 1453–1467 (2018).
29. Elder, M., Bengtsson, M. & Akenji, L. An optimistic analysis of the means of implementation for sustainable development goals: Thinking about goals as means. Sustain. 8, (2016).
30. ICG. A guide to SDG interactions: From science to implementation. (2017).
31. Donoghue, D. & Khan, A. Achieving the SDGs and ‘leaving no one behind’. (2019).
32. Kumar, P., Ahmed, F., Singh, R. K. & Sinha, P. Determination of hierarchical relationships among sustainable development goals using interpretive structural modeling. Environ. Dev. Sustain. 20, 2119–2137 (2018).
33. Barbier, E. B. & Burgess, J. C. The sustainable development goals and the systems approach to sustainability. Econ. E-Journal 11, (2017).
34. Sebestyén, V., Bulla, M., Rédey, Á. & Abonyi, J. Data-driven multilayer complex networks of sustainable development goals. Data Br. 25, 104049 (2019).
35. Fonseca, L. M., Domingues, P. & Dima, A. M. Mapping the Sustainable Development Goals Relationships. 1–15 (2020).
36. Pradhan, P., Costa, L., Rybski, D., Lucht, W. & Kropp, J. P. A Systematic Study of Sustainable Development Goal ( SDG ) Interactions. Earth’s Futur. 1169–1179 (2017). doi:10.1002/eft2.266
37. Nilsson, M. et al. Mapping interactions between the sustainable development goals: lessons learned and ways forward. Sustain. Sci. 13, 1489–1503 (2018).
38. McCollum, D. L. et al. Connecting the sustainable development goals by their energy inter-linkages. Environmental Research Letters 13, (2018).
39. Fuso Nerini, F. et al. Mapping synergies and trade-offs between energy and the Sustainable Development Goals. Nat. Energy 3, 10–15 (2018).
40. Nilsson, M., Griggs, D. & Visback, M. Map the interactions between Sustainable Development Goals. Nature 534, 320–322 (2016).
41. Moyer, J. D. & Bohl, D. K. Alternative pathways to human development: Assessing trade-offs and synergies in achieving the Sustainable Development Goals. Futures 105, 199–210 (2019).
42. Blanc, D. Le. Towards integration at last? The sustainable development goals as a network of targets. 1, (2015).
43. Dörgő, G., Honti, G. & Abonyi, J. Automated analysis of the interactions between sustainable development goals extracted from models and texts of sustainability science. Chem. Eng. Trans. 70, 781–786 (2018).
44. Fukuda-Parr, S. From the Millennium Development Goals to the Sustainable Development Goals: shifts in purpose, concept, and politics of global goal setting for development. Gend. Dev. 24, 43–52 (2016).
45. Barbier, E. B. & Burgess, J. C. Sustainable development goal indicators: Analyzing trade-offs and complementarities. World Dev. 122, 295–305 (2019).
46. Breuer, A., Janetschek, H. & Malerba, D. Translating Sustainable Development Goal (SDG) Interdependencies into Policy Advice. Sustainability 11, 2092 (2019).
47. Cervantes, M. & Hong, S. J. STI policies for delivering on the sustainable development goals. in OECD Science, Technology and Innovation Outlook 2018: Adapting to technological and societal disruption (ed. OECD) (OECD Publishing, 2018). doi:10.1787/fe9c243a-es
48. Adenle, A. A., Chertow, M. R., Moors, E. H. M. & Pannell, D. J. Science, Technology, and Innovation for Sustainable Development Goals. Science, Technology, and Innovation for Sustainable Development Goals (Oxford University Press, 2020). doi:10.1093/oso/9780190949501.001.0001
49. Gusmão Caiado, R. G., Leal Filho, W., Quelhas, O. L. G., Luiz de Mattos Nascimento, D. & Ávila, L. V. A literature-based review on potentials and constraints in the implementation of the sustainable development goals. J. Clean. Prod. 198, 1276–1288 (2018).
50. Reyers, B., Stafford-Smith, M., Erb, K. H., Scholes, R. J. & Selomane, O. Essential Variables help to focus Sustainable Development Goals monitoring. Current Opinion in Environmental Sustainability 26–27, 97–105 (2017).
51. Salvia, A. L., Leal Filho, W., Brandli, L. L. & Griebeler, J. S. Assessing research trends related to Sustainable Development Goals: local and global issues. J. Clean. Prod. 208, 841–849 (2019).
52. ISSC. Review of Targets for the Sustainable Development Goals: The Science Perspective. (2015).
53. United Nations. The Sustainable Development Goals Report 2020. (2020).
54. Namubiru-Mwaura, E. & Marincola, E. Africa Beyond 2030: Leveraging knowledge and innovation to secure Sustainable Development Goals. (2018).
55. Schmalzbauer, B. S. et al. The Contribution of Science in Implementing the Sustainable Development Goals. German Committee Future Earth (German Committee Future Earth, 2016).
56. Fritz, S. et al. Citizen science and the United Nations Sustainable Development Goals. Nat. Sustain. 2, 922–930 (2019).
57. Quinlivan, L., Chapman, D. & Sullivan, T. Validating citizen science monitoring of ambient water quality for the United Nations sustainable development goals. Sci. Total Environ. 134255 (2019). doi:10.1016/j.scitotenv.2019.134255

This is the second of two STRINGS blogs which explore features and characteristics of science, technology and innovation (STI) policy and interventions that seem crucial to achieving the Sustainable Development Goals (SDGs): open access and transdisciplinarity and building of local capabilities.

This blog focuses on the importance of local capabilities. The focus is predominantly on health but much of what is said has broader relevance. The broad argument is that although the SDGs are global goals, they won’t be achieved unless there is support for a variety of types of local capacity building and support for context specific policy and advice.

The first blog in this series was Part I: Maximising STI impact on the SDGs – open science: a case study on Chagas disease.

Global also needs local

The importance of local capacity building to complement global initiatives has been a focus of attention in health research but is relevant to other areas.

Beaglehole and Bonita et al (2010) offer a definition of global health which is broadly shared by many academics and policy makers in the field: “collaborative trans-national research and action for promoting health for all”.   

Yet, we know that in order to maximise development benefits from investment in research and innovation, investment needs to support local capacity building. The articulation of local knowledge and local priorities has been explored from various angles by a diverse range of academics and policy researchers (to mention a few: Bell and Pavitt 1993; Cirera and Maloney 2017: Srinivas and Sutz 2008;  Kraemer-Mbula et al, 2020; Lebel and McLean, 2020).

A key issue is the development of absorptive capacity which enables the rewards of STI investment to take root and serve the needs of the communities that research and innovation purports to benefit. In this vein, Mugwagwa and colleagues write about the importance of ‘local health’ as a vital component of global health (Mugwagwa, 2020; Mackintosh et al 2018).

Without local research, innovation and associated science advisory capacity, health related and other global goals remain in the realm of ambitious targets which cannot be grounded in, informed by and made relevant to local contexts.

Mackintosh and colleagues argue that whilst global value chains provide vital supplies of pharmaceuticals, without local production low- and middle-income countries (LMICs) remain vulnerable in their ability to deal with health challenges.

An edited collection with its roots in Economic and Social Research Council (ESRC) and Department for International Development (DFID) funding details the ways in which Africa has in the past grown an effective pharmaceutical production capacity and could do so in the future. These capabilities and capacities are important because they enable a more powerful response to local health needs and challenges, and an influence over global agendas.

In a related article Mackintosh et al (2018) explain that the “concept of ‘local health’, as it emerges in…current African policy debate, is rooted in a dialogue between proximity and positionality. ‘Proximity’ refers to cumulative local interactions and mutual influences arising from co-location… ‘Positionality’… refers to the influence of location of agency on the framing of issues and priorities, with attendant claims to power and legitimacy in policy making”.

A current, highly interdisciplinary Global Challenges Research Fund (GCRF) project, led by Maureen Mackintosh, explores connections between industrial and health systems in improving cancer care in Tanzania and Kenya. One of the high level messages emerging from this and previous research is that maximising the developmental impact of STI requires us to think beyond the global funding of relevant research. We need also to think about capacities and capabilities in relation the geographies of knowledge generation and use.

Research looking at the contribution of research and innovation efforts to dealing with COVID-19 may well provide further evidence to support an argument that there are tangible connections between local scientific, innovation and industrial capabilities on the one hand and the ability of health systems to provide adequate care on the other hand, and encourage researchers to look more carefully at the linkages.

Capacity building as if local mattered

Some capacity building research focuses on an appreciation of the two-way flow between low-income and wealthier countries to maximise the impact of research and innovation. Local capacity and capabilities support effective use of global research – knowledge and research flowing from LMICs to high-income countries (HICs), as well as HICs to LMICs, enhance efficiencies.

This observation is not new and has been integrated into major capacity building initiatives such as the European and Developing Country Clinical Trials Programme (EDCTP). Some global health public private partnerships have long since incorporated this into their organisation and implementation of research and innovation planning (Chataway et al, 2010).

A recent paper from a study commissioned by DFID’s East Africa Hub (Frost et al, 2019) on knowledge system innovation (KSI) in Kenya, Rwanda and Tanzania makes this point more generally and argues for a reconceptualisation of STI investment, intervention and policy to fully build on opportunities to integrate local and global knowledge flows and systems. This requires acknowledgement of the importance of specific local contexts.

The analysis from this project has identified ways in which enabling environments for research and innovation and associated capacity and capabilities can be created so that global and local research efforts yield increased economic impact. But these relationships are complex, to some extent depend on specific contexts and have to be thought through in a systematic way.

For example, a planned and forthcoming report from the KSI project indicates that the role of education and in-country technological capabilities varies for countries with different KSI. In the case of a stronger knowledge system (according to standard STI indicators), technological capabilities are more relevant but education is less relevant for GDP growth, and can even be a drain if the education workforce is not absorbed by the labour market, causing a brain drain. In the context of weaker knowledge systems, basic institutions and education seem to be required for technological capabilities and infrastructures such as ICT to have an impact on GDP growth.

In low-income contexts looked at in the study, education has a more straightforward positive impact on GDP. There appears to be a virtuous cycle in weaker knowledge system contexts that can be exploited. In the short-term technological capabilities and ICT have a strong positive impact on the education, and education has a positive impact on GDP. GDP has a long germ impact on technological capabilities and ICT, which feedback on education, and so on.

Using bibliometric analysis, the report also highlights that although global research funding in Kenya, Rwanda and Tanzania is largely concentrated on health (i.e. SDG 3: Ensure healthy lives and promote well-being for all at all ages), it is not the only SDG where the three countries target indicators perform poorly and where the country may benefit from building national research capability (Mormina, 2019).

At the same time, results suggest that despite so much funding going into health research, the SDG 3 target indicators have not improved substantially, suggesting that so much research may have not prioritised building of local capabilities. Increased local relevance will require research partnerships based on the articulation of local priorities and research requirements for implementation of research, for example investment in health systems research as well as biomedical research.

COVID-19 will provide us with additional evidence of the importance of funding research and innovation capabilities in LIMCs. This pandemic, like HIV/AIDS and other diseases which have impacted particularly negatively on poor people living in environments where resources are limited, require local research efforts as well as global efforts to develop vaccines and treatments.

Moreover, capacity built in one area can help in more rapid response in different areas. For example, the EDCTP’s COVID-19 response is possible because of previous and unrelated research and innovation capacity that has been built.

Science policy as if local science advice mattered

As part of the more general argument that local capabilities are important, is a particular point crucial to the success of initiatives to effectively align STI to the development goals. Science advice to policymakers and decision makers needs to be rooted in expertise about local contexts.

The SDGs are global. Many of the targets, metrics and models associated with the SDGs are based on global analysis and trends. Science advice at a global level relating to international treaties and initiatives reflects this.

But to get to global success we need to make the translation to local contexts. With this in mind, the work of The International Network for Government Science Advice (INGSA), with its aims to build capabilities at all levels of government, is critical.

This is the first of two STRINGS blogs which explore features and characteristics of science, technology and innovation (STI) policy and interventions that seem crucial to achieving the Sustainable Development Goals (SDGs): open access and transdisciplinarity and building of local capabilities. The second blog in this series is Part II: Maximising STI impact on the SDGs – local capacity building.

It is no wonder that Chagas disease was included in the list of neglected tropical diseases by the World Health Organization in 2007 (WHO, 2020). Over 100 years have passed since Chagas was first discovered and there is still no appropriate solution to this problem, which mainly affects marginalized communities around the globe.

Chagas constitutes a socio-environmental problem (Sanmartino, 2015) that interacts with several Sustainable Development Goals (SDGs) in addition to good health and wellbeing (SDG 3). For instance, education and access to information is key for prevention; better infrastructure, including roads and hospitals, is important for early detection and treatment; while changes in ecological systems, due to production activities and climate change, have moved the vector (i.e. the kissing bugs that may transmit the disease) towards new, frequently urban, areas.

What is open science, and how can it help?

We define open science practices as those that foster collaboration throughout the research process and/or that openly share the intermediate and final outcomes of research.

Open science literature anticipates benefits in terms of research efficiency and responsiveness to social needs. Efficiency is expected to be boosted through collaboration since more creative, rapid, cost effective responses could emerge due to interaction and expanded participation (Ben-David, 1960; Bonney et al., 2009; Jeppesen & Lakhani, 2010; Nielsen, 2013). Open access to scientific resources across the research community can also avoid duplication of efforts and minimize economic and time costs.

Responsiveness to social needs can be enhanced through collaboration because wider participation in the production of scientific knowledge can better guide the research agenda towards addressing challenges (European Commission, 2016; Hecker et al., 2018; Stodden, 2010). Open access can also help boost responsiveness by increasing visibility (Stodden, 2010) and promoting cheaper solutions to problems.

Given the complex challenges associated with Chagas, and the limited progress that has been made in the last 100 years, it is important to consider how different research  practices can help. With a focus on collaboration and sharing, open science practices offer an opportunity to adopt a truly multi-dimensional approach, increase efficiency and responsiveness and drive progress towards solving the Chagas problem.

Open science in action – an analysis of five Chagas projects

We analyzed five different research projects on Chagas, selected because they had been pointed out as innovative and successful in terms of reaching their goals.[1] They covered the fields of education, epidemiology, biology and medicine.

Three of the research groups recognized being active users of open and collaborative methodologies, while two of them did not identify themselves as part of the open science community. However, all projects opened some of their research practices in terms of access or collaboration to a greater or lesser extent.

We performed 13 semi-structured interviews with project members or users during the second semester of 2019. We found evidence of three mechanisms through which open science may enhance research impact in terms of SDGs.

1. Improving research efficiency

Open science practices improve research efficiency by reducing research time and costs through open access to resources and by extending collaboration based on citizen science practices.

For example one of the selected projects, TDR Targets[2], developed an open access database and computational tool which integrates genomic and chemical data to guide investigations on new treatments against human pathogens. One of its members mentioned that:

“[The TDR Targets open database] will save time and money for researchers who either don’t have the time or don’t have the money to do these kind of activities, especially in poor countries”. 

We found another example of collaboration driving efficiency in Geovin[3], a citizen science project that developed an app for members of the public to submit data about the geographic distribution of the Chagas vector. By using citizen science methodologies to gather data directly from the public, the team was able to build a far more comprehensive database than would have been possible through closed methodologies. One researcher explained:

“This tool allowed us to access kissing bugs in places where we could not go to look for information … we can’t travel across all Argentina looking for kissing bugs”.

2. Making research more responsive to social needs

Projects that adopt collaborative practices and interaction with actors dealing directly with Chagas tend to respond more effectively to social needs.

In the case of Geovin, project members identified social needs and problems through direct interaction with communities, and consequently adapted several aspects of their project.

“The initial motivation was purely scientific. We were looking for data on the distribution of the kissing bug … However, with the use of the app we began to see it as an educational tool …  We saw people needed a rapid diagnosis of whether they were in presence of a kissing bug or not [in most occasions the diagnosis is negative].”

Similar evidence was found in a mostly closed project aiming to develop an early diagnosis kit for Chagas using the Polymerase Chain Reaction (PCR) method to detect congenital transmission in newborns. In the clinical trial phase, when external hospitals and health centers got involved, the project provided a great opportunity to instruct the health personnel on how to systematize their activities to respect sanitary protocols. Thus, through collaboration and interactions with actors outside the research team, such as healthcare professionals, the project improved the quality of health services.

3. Expanding impact to more SDGs

We found that research groups which adopted collaborative practices tend to better identify the multiple dimensions involved in Chagas. This allowed the projects to expand the scope of SDGs tackled through the research and implementation processes, and gave more importance to the SDGs that needed more attention.

This is the case of the group “¿De qué hablamos cuando hablamos de Chagas?”[4] which aims to provide theoretical and practical tools for critical reflection in different educational contexts. In this case, transdisciplinary collaboration with people from different environments gave project members the opportunity to better identify different dimensions through which to tackle Chagas. This was mentioned on many occasions during interviews:

“We see that Chagas is an overly complex problem. If you start “trimming” the problem, you start setting aside [useful] solutions.”

“We wish to generate consciousness that Chagas is one example of complex problems that should be approached in its entirety … offer tools to attack these types of problems from multiple dimensions.”

“During our collaboration with Defensoria del Pueblo, we approached Chagas through the perspective of human rights.”

Another example is the case of Geovin. The original aim of the project was to fill a gap in the data available on the distribution of the kissing bug in Argentina. However, project members mentioned that since the app has been launched, the impact of Geovin has evolved to other areas. Geovin researchers mention two important effects. Firstly, it has had a “calming” effect on the users of the app by letting them know they are not in the presence of a kissing bug in the case of negative results. One team member said:

“We didn’t design it [the Geovin app] for this reason … when we started seeing the information that people sent through the app, we saw it as a tool that makes people calm down rapidly when they mistakenly think they are in the presence of a kissing bug.”

Secondly, it had an educational effect because citizen science gives researchers a tool to easily reach the population, which can be very important in education on Chagas prevention. The citizen science (collaborative) methodology is essential for creating these spillovers as it allows for close contact with the population to communicate important information and precautions for Chagas prevention, which would have been impossible through a closed approach.

Another mechanism through which impact was expanded to more SDGs was originated by open access practices, creating synergies towards new SDGs. For example, the TDR project had an indirect economic effect by providing open access datasets for researchers from underdeveloped regions who do not have the resources to perform this type of data-intensive tasks themselves. In this sense, the project has had an equalizing effect, helping to bridge the gap between different scientific communities by providing researchers with these valuable resources. As one interviewee put it:

“[M]any of these [parasite] genomic projects had an indirect economic impact … and even though most scientists would like to do large-scale genome analysis using computational biology tools, they often don’t have resources at hand to analyze these datasets. So, for people who don’t have the resources and for people who don’t have the expertise, databases like TDR Targets makes it easier for them to not only access the data but also do the analysis.”

In summary, Chagas problems are multi-dimensional and solutions must be integrated, as recognized by most of the stakeholders we interviewed. Our case study provides further evidence on how scientific production could contribute more effectively, efficiently and in a wider scope towards the SDGs if it adopted a more open and collaborative approach in which diverse knowledge, experience and actors take part.

Footnotes

[1] We interviewed seven key informants during the first semester of 2019.

[2] See tdrtargets.org and Urán Landaburu et al. (2019)

[3] See http://geovin.com.ar/ and Basalobre et al. (2019)

[4] See https://hablamosdechagas.org.ar/ and Sanmartino (2015)

References

Balsalobre, A., Ceccarelli, S., Cano, M. E., Ferrari, W. A., Cochero, J., & Martí, G. A. (2019). Apps en el desarrollo de ciencia ciudadana: GeoVin. I Jornadas de Inclusión de Tecnologías Digitales en la Educación Veterinaria, La Plata.

Ben-David, J. (1960). Roles and Innovations in Medicine. American Journal of Sociology, 65(6), 557–568. https://doi.org/10.1086/222786

Bonney, R., Ballard, H., Jordan, R., McCallie, E., Phillips, T., Shirk, J., & Wilderman, C. C. (2009). Public Participation in Scientific Research: Defining the Field and Assessing Its Potential for Informal Science Education. A CAISE Inquiry Group Report. In Online Submission. Center of Advancement of Informal Science Education (CAISE). https://eric.ed.gov/?id=ED519688

European Commission. (2016). Open innovation, open science, open to the world—A vision for Europe.

Hecker, S., Bonney, R., Haklay, M., Hölker, F., Hofer, H., Goebel, C., Gold, M., Makuch, Z., Ponti, M., Richter, A., & others. (2018). Innovation in citizen science–perspectives on science-policy advances. Citizen Science: Theory and Practice, 3(1).

Jeppesen, L. B., & Lakhani, K. R. (2010). Marginality and Problem-Solving Effectiveness in Broadcast Search. Organization Science, 21(5), 1016–1033. https://doi.org/10.1287/orsc.1090.0491

Nielsen, M. (2013). Reinventing Discovery: The New Era of Networked Science. Princeton University Press.

Sanmartino, M. (Coord. ). (2015). Hablamos de Chagas. Aportes para (re)pensar la problemática con una mirada integral. Contents: Amieva, C., Balsalobre, A., Carrillo, C., Marti, G., Medone, P., Mordeglia, C., Reche, V.A., Sanmartino, M., Scazzola, M.S. Consejo Nacional de Invetigaciones Cientificas y Tecnicas (CONICET).

Stodden, V. (2010). Open science: Policy implications for the evolving phenomenon of user-led scientific innovation. Journal of Science Communication, 09(01). https://doi.org/10.22323/2.09010205

Urán Landaburu, L., Berenstein, A. J., Videla, S., Maru, P., Shanmugam, D., Chernomoretz, A., & Agüero, F. (2019). TDR Targets 6: Driving drug discovery for human pathogens through intensive chemogenomic data integration. Nucleic Acids Research, gkz999. https://doi.org/10.1093/nar/gkz999

WHO. (2020, March 11). Chagas disease (also known as American trypanosomiasis). https://www.who.int/en/news-room/fact-sheets/detail/chagas-disease-(american-trypanosomiasis)

The shift in R&D goals towards the SDGs is driving demand for new S&T indicators…

The shift in S&T policy from a focus on research quality (or ‘excellence’) towards societal impact has led to a demand for new S&T indicators that capture the contributions of research to society, in particular those aligned with SDGs. The use of the new ‘impact’ indicators would help monitoring if (and which) research organisations are aligning their research towards certain SDGs.

Responding to these demands, data providers, consultancies and university analysts are rapidly developing methods to map projects or publications related to specific SDGs. These ‘mappings’ do not analyse the actual impact of research, but hope to capture instead if research is directed towards problems or technologies that can potentially contribute to improving sustainability and wellbeing.

…but indicators on the contributions of science on the SDGs are not (yet) robust

Yet this quick surge of new methods raises new questions about the robustness of the mappings and indicators produced, and old questions about the effects of using questionable indicators in policy making. The misuse of indicators and rankings in research evaluation has been one of the key debates in science policy this last decade, as highlighted by initiatives such as the San Francisco Declaration on Research Assessment (DORA), the Leiden Manifesto or The Metric Tide report in the UK context.

Indeed, the first publicly available analysis of SDG impact, released recently by the Times Higher Education (THE), should be a motive for serious alarm. For almost two decades, the THE has offered a controversial ranking of universities according to ‘excellence’. This last May it has produced a new ranking of universities according to an equally questionable composite indicator that arbitrarily adds up dimensions of unclear relevance. For example, the indicator of the impact on health (SDG3) of a university depends on the one hand on its relative specialisation on health – as captured, e.g. by the proportion of papers related to health (10% of total weight), and on the other hand on the proportion of health graduates (34.6%). But the weight is also based on (self-reported) university policies such as care provided by the university, e.g. free sexual and reproductive health services for students (8.6%) or community access to sports facilities (4%). This indicator is likely to cause more confusion than clarity and it is potentially harmful as it mystifies university policies for the SDGs.

The relative specialisation on health captured by the proportion of papers related to health in the THE ranking is partly supported by an Elsevier analysis of the publications that are related to the SDGs – which might seem more reliable than those based on data self-reported by universities.

However, mapping publications to the SDGs is not as straightforward as it might seem. An article published last month by a team at the University of Bergen (see Armitage et al., 2020) sounded the alarm by showing that slightly different methods may produce extremely different results. When comparing the papers related to SDGs retrieved with their own analysis with those by Elsevier, they found that there is astonishingly little overlap – in most SDGs only around 20-30% as illustrated in Figure 1. The differences also affected the rankings of countries’ contributions to the SDGs. The Bergen team concluded that ‘currently available SDG rankings and tools should be used with caution at their current stage of development.’

Figure 1. Comparison between the Bergen and Elsevier approaches to mapping SDG-related publications. Based on Web of Science Core collection, 2015-2018. Source: Armitage et al. (2020)

Why are mappings of publications to SDGs so different? Lack of direct relation between science and SDGs

Perhaps we should not be surprised that different methods yield so different results. The SDGs refer to policy goals about sustainability in multiple dimensions – ending poverty, improving health, achieving gender equality, preserving the natural environment, et cetera. Science and innovation studies have shown that the contributions of research to societies are often unexpected and highly dependent on the local social contexts in which knowledges are created and used.

Nevertheless, most research is funded according to the expectations of the type of societal benefits that it may generate – and thus one can try to map these expectations or promises according to the language used in the (titles and abstracts of) projects and articles. Unfortunately, the expected social contributions are often not made explicit in these technical documents because the experts reading them are assumed to see the potential value.

As a consequence, the process of mapping projects or articles to the SDGs is ineluctably carried out through an interpretative process that ‘translates’ (or attempts to link) scientific discourse into potential outcomes. Of course, such translation is dependent on the analysts’ understandings of science and the SDGs. There is consensus on some of these understandings. For example most analysts would agree that research on malaria is important for achieving global health. However, other translations are highly contested: should nuclear (either fission or fusion) research be seen as a contribution to clean and affordable energy? Should all educational research be counted as contribution to the SDG on ‘quality education’?

Furthermore, in a number of SDGs such as gender equity (SDG 5) or reduced inequalities (SDG 10), there is a lot of ambiguity on the potential contributions. In particular, there is relatively little research specifically on these issues in comparison to the research with outcomes affecting gender relations and inequalities.

Another challenge of these mappings is that the databases used for analysis are not comprehensive, having a much larger coverage of certain fields and countries (See Chapter 5 in Chavarro, 2017). This is particularly problematic when analysing research of the Global South.

In summary, there are many societal problems where there is lack of consensus and ambiguities, and in these cases, the mappings will depend on the particular interpretation of the SDGs that the mapping methods implicitly adopt.

A plurality of SDG mapping methodologies

It follows from the previous discussion that different analyses carry out different ‘translations’ of the SDGs into science through the choice of different methodologies. The study by Clarivate (2019) is based on a core set of articles that mention ‘Sustainable Development Goals’ – thus it is related to research areas with an explicit SDG discourse.

The approaches developed by Bergen University, Elsevier, the Aurora Network and SIRIS Academic are based on searching for strings of keywords, in particular keywords found in the UN SDGs targets or other relevant policy documents. These searches are then enriched differently in each case. The hypothesis of this ‘translation’ is that publications or projects containing these keywords are those best aligned with the UN SDG discourse. The question is then where should the line be drawn. For example, why in some lists zika virus is included in the list of health SDG3, but not the closely related dengue virus, with a much higher disease burden?

An alternative approach being developed at NESTA and Dimensions uses policy documents and keywords to train machine learning algorithms in order to identify articles related to the SDGs instead of creating a list of keywords to search the articles. The downside of this approach is that is it a black box regarding the preferences (or biases) of the machine learning algorithms.

Comparisons as a pragmatic way forward

In the face of this plurality of approaches potentially yielding disparate results, the STRINGS project aims to be a space for constructive discussion and comparison across different methodologies. A comparison between methods will help in finding out to which extent there is consensus or dissensus in the mappings of various SDGs.

To this purpose, in collaboration with the Data Intensive Science Centre at the University of Sussex (DISCUS), on July 23-27 we have carried out a hackathon focussed on retrieving publications related to clean energy research (SDG 7) (to be reported). We have also organised a workshop to discuss the results obtained by the different teams mentioned above with the various methodologies, and how each methodology might capture a particular ‘translation’ or understanding of the SDGs. As proposed by the Bergen team, this comparison ‘will allow institutions to compare different approaches, better understand where rankings come from, and evaluate how well a specific tool might work’ for specific contexts and purposes.

Disclaimer

The discussion in this blog builds on ongoing work carried out by the STRINGS project. It presents my personal view (rather than the project’s) following my engagement debates on the use of indicators in policy and evaluation, for example a recent participation in an EC Expert Group on ‘Indicators for open science’.