RE: How to evaluate science, technology and innovation in a R4D context? New guidelines offer some solutions | Eval Forward

Dear Svetlana,

Hi and thanks for the opportunity to comment on the guidelines. I enjoyed reading them, yet only had time to respond to the first two questions.

My responses come with a caveat - I do not have a research background, yet observed during a time i worked with agricultural scientists that the then current preoccupation with assessing impact among the ultimate clients group, as gauged by movements in the relative values of household assets, tended to mask the relative lack of information and interest about the capacity and capabilities of local R&D / extension systems before, during, and after investment periods. Their critical role in the process often got reduced to being treated as assumptions or risks to "good" scientific products or services.

This made it difficult to link any sustainable impact among beneficiaries with information on institutional capacity at the time that research products were being developed. This may also have explained how believing in (hopelessly inflated) rate of return studies required a suspension of belief,  thus compromising prospects for efforts in assessing the impact of research to make much difference among decision-makers.

Moving on - My responses to the two of your questions follow, and hope some you find interesting, useful even. 

1.    Do you think the Guidelines respond to the challenges of evaluating quality of science and research in process and performance evaluations?

Responding to this question assumes/depends on knowing the challenges to which the guidelines refer. In this regard, Section 1.1 is a slightly misleading read given the title. Why?

The narrative neither spells out how the context has changed nor therefore, how and why these pose challenges to evaluating the Quality of Science. Rather, it describes CGIAR’s ambition for transformative change across system transformation – a tautology? - resilient agri-food systems, genetic innovation, and five  - unspecified  - SDGs. And, it concludes by explaining that, while CGIAR funders focus on development outcomes, the evaluation of CGIAR interventions must respond to both the QoR4D – research oriented to deliver development outcomes – and OECD/DAC – development orientation – frameworks. 

The reasons that explain the insufficiency of the 6 OECD DAC criteria in evaluating CGIAR’s core business do not appear peculiar to CGIAR’s core business, relative to other publicly funded development aid  - the unpredictable and risky nature of research and the long time it takes to witness outcomes. Yes, it may take longer given the positioning of the CG system but, as we are all learning, operating environments are as inherently unpredictable as the results. Context matters. Results defy prediction; they emerge. Scientific research, what it offers, and with what developmental effect is arguably not as different as the guidelines suggest.  About evaluating scientific research, the peculiarity is who CGIAR employ and the need to ensure a high standard of science in what they do – its legitimacy and credibility. The thing is, it is not clear how these two elements, drawn from the QoR4D frame of reference, cover off so to say the peculiarities of CGIAR’s core business and so fill the gap defined by the 6 OECD DAC criteria. Or am I missing something?

The differences between Process and Performance Evaluations are not discernible as defined at the beginning of Section 2.2. Indeed they appear remarkably similar; and so much so I asked myself – why have two when one would do? Process evaluations read as summative self-assessments across CGIAR and outcomes are in the scope of Performance Evaluations. Performance Evaluations read as more formative and repeat similar lines of inquiry - assessing organisational performance and operating models as well as process to Process Evaluations – the organisational functioning, instruments, mechanisms and management practices together with assessments of experience with CGIAR frameworks, policies etc.. No mention of assumptions – why given the “unpredictable and risky nature of research?” Assumptions, by proxy, define the unknown and for research managers and (timely) evaluations, they should be afforded an importance no less than the results themselves. See below

The explanation as to the differences between the Relevance and Effectiveness criteria as defined by OECD/DAC with QoR4D in Table 2 is circumscribed. While the difference to do with Relevance explicitly answers the question of why CGIAR?, that for effectiveness is far too vague (to forecast and evaluate). What is so limiting about how the reasons why CGIAR delivers knowledge, products, and services  - to address a problem and contribute to innovative solutions  - can not be framed as objectives and/or results? And especially when the guidelines claim Performance Evaluations will be assessing these. 

2. Are four dimensions clear and useful to break down during evaluative inquiry (Research Design, Inputs, Processes, and Outputs)? (see section 3.1)

This section provides a clear and useful explanation of the four interlinked dimensions – Research Design, Inputs, Processes, and Outputs in Figure 3 that are used to provide a balanced evaluation of the overall Quality of Science. 

A few observations:

“Thinking about Comparative Advantage during the project design process can potentially lead to mutually beneficial partnerships, increasing CGIAR’s effectiveness through specialization and redirecting scarce resources toward the System’s relative strength”.…

1)    With this in mind, and as mentioned earlier in section 2.3, it would be useful to explain how the research design includes proving, not asserting, CGIAR holds a comparative advantage by going through the four-step process described in the above technical note. Steps that generate evidence with which to claim CGIAR does or does not have a comparative advantage to arrive at a go/no go investment decision. 

2)    Table 3 is great in mapping the QoS’s four dimensions with the six OECD/DAC criteria and I especially liked the note below on GDI. I remain unclear, however, why the Coherence criterion stops at inputs and limits its use to internal coherence. External coherence matters as much, if not more, and especially concerning how well and to what extent the outputs complement and are harmonised and coordinated with others and ensure they add value to others further along the process.  

3)    While acknowledging the centrality of high scientific credibility and legitimacy, it is of equal importance to also manage and coordinate processes to achieve and maintain the relevance of the outputs as judged by the client. 

4)    I like the description of processes, especially the building and leveraging of partnerships  

5)    The scope of enquiry for assessing the Quality of Science should also refer to the assumptions, specifically those that have to hold for the outputs to be taken up by the client organisation, be they a National Extension Service or someone else. Doing this should not be held in abeyance to an impact study or performance evaluation. I say this for, as mentioned earlier, the uncertainty and unpredictability associated with research is as much to do with the process leading up to delivering outputs as it is in managing the assumption that the process along the impact pathway, once the outputs have been “delivered”, will continue. This mustn’t be found out until too late. Doing this helps mitigate the risk of rejection. Scoring well on the Quality of Science criterion does not guarantee the product or service is accepted and used by the client remembering that it is movement along the pathway, not the QoS, that motivates those who fund CGIAR.