win more, lose less: capturing synergies between for ...€¦ · cgiar’s comparative advantage to...

Maggie Gill

Quality of research for development and the science policy interface

STELLENBOSCH, SOUTH AFRICA10 - 12 OCTOBER, 2018

Win more, lose less:

Capturing synergies betweenSDGs through agricultural research

ISPC Chair

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Science Policy Interface?

The science and policy communities have different languages, different cultures and work to different

time-scales

There is a published literature on these differences…

Research managers aspiring to contribute to policy should familiarise themselves with the differences…..

Win more, lose less:Capturing synergies betweenSDGs through agricultural research

Why is a CGIAR frame of

reference for quality desirable?

• Increased accountability for public funding means research commissioned by public funders needs to be of highest “quality”

• New ways of doing research (e.g. inter-disciplinarity and modelling) blur edges around criteria of quality

• Multiple CGIAR governance structures with differing criteria for quality


Why is a CGIAR frame of

reference for quality desirable?

• Increased accountability for public funding means research commissioned needs to be of highest “quality” – beyond traditional metrics

• New ways of doing research (e.g. inter-disciplinarity and modelling) blur edges around criteria of quality – issues around data quality

• Multiple CGIAR governance structures with differing criteria for quality – need for coherence


‘Quality’ Working Group

• ISPC convening role

• Started as Quality of Science

• Changed to Quality of Research for Development to recognise multiple criteria

• Workshop involved representatives from different constituencies across the System including Board Science Committees and CRP Steering Committees as well as Center Deputy Director-Generals for Research and CGIAR Research Program leaders and System Management Office and included input from an external panel

• Adoption - Frame of Reference will be included in CGIAR Business Plan presented for approval to System Council meeting in November

4 Elements of QoR4D - 1

Relevance refers to the importance, significance and usefulness of the research objectives, processes and findings to the problem context and to society, and CGIAR’s comparative advantage to address the problems. It incorporates strategic stakeholder engagement along the AR4D continuum, explicit impact pathways, original and socially relevant research aligned to national and regional priorities, as well as the CGIAR SRF and SDGs.



Scientific credibility requires that research findings be robust and that sources of knowledge be dependable and sound. This includes a clear demonstration that data used are accurate, that the methods used to procure the data are fit for purpose, and that findings are clearly presented andlogically interpreted. It also recognizes the importance of good scientific practice, such as peer review.



Legitimacy means that the research process is fair and ethical and perceived as such. This encompasses the ethical and fair representation of all involved and consideration of interests and perspectives of intended users. It suggests transparency/lack of conflict of interest, recognition of responsibilities that go with public funding, genuine recognition of partners’ contributions as well as partnerships built on trust.



Effectiveness signifies that research generates

knowledge, products and services that stimulate

actions that address the problem and contribute to

solutions and innovations. It implies that research is

designed, implemented and positioned for use within a

dynamic theory of change, with appropriate

leadership, capacity development and support to the

enabling environment to translate knowledge to use

and to help generate desired outcomes.


Relevance

Agricultural research for development should be demand-led – how often is it science push?

How many research proposals now claim to be able to make a “significant contribution” to the SDGs even if the budget is $100,000

Research proposals should be realistic in their claims


Scientific Credibility

Policy-makers and scientists use different time-scales –

policy-makers want urgent answers – when is “it’s the

best data we have” good enough?

Drawing on comment from Jean-Pascal van Ypersele

“Few mistakes in IPPC report 10 years ago caused major problems of credibility for IPCC”

Researchers need to take account of the robustness of the irdata in designing research questions


Legitimacy

Thousands of household surveys are conducted in Africa every year – have all of them gone through ethics panels? Do we think about the time we are taking from stakeholders’ working lives?

Do commodity-based institutes have a conflict of interest? How do they avoid a perception that they start research from the basis that their commodity is the solution?

The importance of legitimacy to quality is the least recognized of the 4 elements


Effectiveness

Quote from de Sherbinin (2014) paper on climate change hotspots: Climatic Change 123:23–37

“A common assertion by map authors is that their results will beuseful to policy audiences. Yet hotspots efforts tend to be plagued by a lack of specificity regarding who precisely constitutes the “policy audience”.

From Tomich et al concluding chapter of special issue from SF16

"Linking local to regional and global scales is key to achieving impacts at scales sufficient to induce the transformative changes required to achieve the SDGs

“Policy” is a “slippery” word! Researchers need to be clear WHO takes decisions on policies they want to influence


Thoughts for discussion


• The research and policy communities have different languages, different cultures and work to different time-scales

• Few individual researchers can meet all 4 elements – a team approach is required

• R4D does not mean that researchers should be held accountable for delivery of development outcomes but they do need to show that they have taken serious steps to influence development outcomes

• The Frame of Reference could be used to help researchers understand where they “fit” in the R4D continuum and thereby enhance the impact of research

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