Hi everyone! I have really enjoyed reading through these many responses and am keen to explore more effective and diverse scaling metrics with this community. I am a research specialist with the Accelerating Impacts of CGIAR Climate Research in Africa (AICCRA) project that aims to bride the research-to-practice divide and scale climate-smart agriculture and climate information services, through fit-for-purpose engagements and collaborative design processes at local, national and regional levels across Africa. I am also writing my PhD on ‘responsible scaling’ – examining models for integrating responsible pathways – those that are inclusive and into the AICCRA project.
The arguably flawed mantra of our agricultural research for development ecosystem is that scaling is always good and should be prioritized. We are pushed by internal institutional incentives (amount of publications, funding raised) to pursue “quick wins” or “low hanging fruit”. We are pressured by donor organizations to showcase our theories of change as linear and replicable models of success. However, many have put this thinking into question – including the authors of the background resources provided above. Of course, we also shouldn’t assume to just stop scaling. There are great merits in leveraging the knowledge and successes developed by someone else or elsewhere to a new site or context to increase the speed of implementation and the odds of obtaining the desired outcomes. Furthermore, we also need to stop assuming that we as “innovators” or “researchers” come in with best-bet solutions, and also account for existing models and common practices, and learn from these – to be able to complement rather than replace traditional knowledge and innovation, with science-based approaches.
What we need to do for attaining sustainable systems change is step away from unrealistic notions of success – such as alone (attributable to a single project) reduce poverty and further food security – and rather set goals (and anti-goals) early on in project design aligned with a recognized desired directionality, but set adaptable indicators to be able to better target end-users and anticipate potential negative outcomes of scaling. This also means setting up a monitoring and evaluation plan that is able to account for transformative systems change over time and space – examining not only the efficient implementation of a project’s results. While scaling and systems change are not the same, they certainly need to draw lessons from one another. The UNDP M&E Sandbox highlights that “how and for whom we measure matters greatly for our capacity to learn, our ability to adapt, and our awareness of whether we are on track with our (portfolio of) interventions that interact with complex systems.” To foster effective agricultural systems, we should thus engage with those closest to the problems at hand to collaboratively find solutions.
Scaling is complex and heterogenous contexts call for diverse and dynamic approaches, that respond to local challenges and priorities. We should anticipate potential risks early, and collaboratively define visions of success with those we are trying to ‘impact’ in order to design not singular, but pluralistic and socio-technical innovation bundles that can best respond and enable relevant and inclusive change. It is also necessary to be reflexive – and look in the mirror – as researchers and question our own biases and assumptions. These are all dimensions of responsible innovation (see Stilgoe et al., 2013), that can be transferred to the science of scaling (see Wigboldus and Brouwers, 2016).
Check out recent publications with amazing co-authors for more!
RE: Evaluating Scaling Efforts: Measuring What Matters
Kenya
Hanna Ewell
Research Specialist
CGIAR
Posted on 13/01/2025
Hi everyone! I have really enjoyed reading through these many responses and am keen to explore more effective and diverse scaling metrics with this community. I am a research specialist with the Accelerating Impacts of CGIAR Climate Research in Africa (AICCRA) project that aims to bride the research-to-practice divide and scale climate-smart agriculture and climate information services, through fit-for-purpose engagements and collaborative design processes at local, national and regional levels across Africa. I am also writing my PhD on ‘responsible scaling’ – examining models for integrating responsible pathways – those that are inclusive and into the AICCRA project.
The arguably flawed mantra of our agricultural research for development ecosystem is that scaling is always good and should be prioritized. We are pushed by internal institutional incentives (amount of publications, funding raised) to pursue “quick wins” or “low hanging fruit”. We are pressured by donor organizations to showcase our theories of change as linear and replicable models of success. However, many have put this thinking into question – including the authors of the background resources provided above. Of course, we also shouldn’t assume to just stop scaling. There are great merits in leveraging the knowledge and successes developed by someone else or elsewhere to a new site or context to increase the speed of implementation and the odds of obtaining the desired outcomes. Furthermore, we also need to stop assuming that we as “innovators” or “researchers” come in with best-bet solutions, and also account for existing models and common practices, and learn from these – to be able to complement rather than replace traditional knowledge and innovation, with science-based approaches.
What we need to do for attaining sustainable systems change is step away from unrealistic notions of success – such as alone (attributable to a single project) reduce poverty and further food security – and rather set goals (and anti-goals) early on in project design aligned with a recognized desired directionality, but set adaptable indicators to be able to better target end-users and anticipate potential negative outcomes of scaling. This also means setting up a monitoring and evaluation plan that is able to account for transformative systems change over time and space – examining not only the efficient implementation of a project’s results. While scaling and systems change are not the same, they certainly need to draw lessons from one another. The UNDP M&E Sandbox highlights that “how and for whom we measure matters greatly for our capacity to learn, our ability to adapt, and our awareness of whether we are on track with our (portfolio of) interventions that interact with complex systems.” To foster effective agricultural systems, we should thus engage with those closest to the problems at hand to collaboratively find solutions.
Scaling is complex and heterogenous contexts call for diverse and dynamic approaches, that respond to local challenges and priorities. We should anticipate potential risks early, and collaboratively define visions of success with those we are trying to ‘impact’ in order to design not singular, but pluralistic and socio-technical innovation bundles that can best respond and enable relevant and inclusive change. It is also necessary to be reflexive – and look in the mirror – as researchers and question our own biases and assumptions. These are all dimensions of responsible innovation (see Stilgoe et al., 2013), that can be transferred to the science of scaling (see Wigboldus and Brouwers, 2016).
Check out recent publications with amazing co-authors for more!
A systems approach for more effective and inclusive agricultural innovation for sustainable transformation: A call to action, reflection and five research commentaries | CGIAR GENDER Impact Platform
https://www.nature.com/articles/s44264-024-00044-y