Gender in agriculture and food systems: An Evidence Gap Map

The 2007-2008 global food-price crisis disproportionately affected women, particularly smallholder women farmers (Sexsmith et al. 2017).1 The subsequent responses by governments, multilateral agencies and other institutions over the last decade do not seem to have had the intended effect of addressing underlying power imbalances in agriculture and food systems (Botreau and Cohen 2020).2 CGIAR has been at the forefront of a mission to change the status-quo through impactful gender research. The CGIAR Generating Evidence and New Directions for Equitable Results (GENDER) Platform catalyzes targeted research on gender equality in agriculture and food systems and collaborates with decision-makers to achieve a new normal: a world in which gender equality drives a transformation towards equitable, sustainable, productive and climate-resilient food systems. Closing the knowledge gaps in gender and agriculture and food systems is a crucial step towards achieving this vision. This Evidence Gap Map (EGM) attempts to consolidate and integrate evidence on gender in agriculture and food systems, and provides a framework for prioritizing research across different themes, enabling focused evidence synthesis and generation. While most existing EGMs (Moore et al. 2021)3 focus on synthesizing evidence on impact estimates of interventions, this EGM presents a broader landscape of evidence across eleven identified themes in gender in agriculture and food systems. This EGM, however, does not synthesise information, but presents a systematic and interactive matrix of outcomes across all themes based on the existing evidence. The map includes studies that use qualitative, quantitative and mixed method designs

Conducting a systematic review: Methodology and steps

Systematic reviews have gained momentum as a key method of evidence synthesis in global development research in recent times. As defined in the Cochrane Handbook on Systematic reviews “Systematic reviews seek to collate evidence that fits pre-specified eligibility criteria in order to answer a specific research question. They aim to minimize bias by using explicit, systematic methods documented in advance with a protocol.” It is important to highlight that a systematic review is different from a literature review. While a literature review qualitatively summarises evidence with no specific protocol or search criteria, a systematic review is based on a clearly formulated question, identifies relevant studies, appraises their quality and summarizes the evidence by use of a selected explicit methodology. It is this explicit and systematic approach that distinguishes systematic reviews from traditional reviews and commentaries. It is also important to distinguish between a systematic review and a meta-analysis. While a systematic review refers to the entire process of selection, evaluation and synthesis of evidence; meta-analysis is a specialised sub-set of systematic review.3 Meta-analysis refers to the statistical approach of combining data derived from systematic review. It uses statistical techniques to combine the data examined from individual research studies and uses the pooled data to come to new statistical conclusions. Hence not all systematic reviews will include a meta-analysis, but a meta- analysis is necessarily in a systematic review. The main purpose of this document is to provide guidelines, recommendations and propose a methodology for conducting mixed- method systematic reviews for evidence synthesis for “gender in agriculture and food systems” for the CGIAR GENDER Platform. In this document we highlight some of the good practices from leading organisations who have contributed to the development of methodology for Systematic Reviews over the years. Throughout the document, we refer to relevant guidelines recommended by these organisations for conducting systematic reviews and adapt it to the proposed questions that include synthesis of qualitative, quantitative and mixed-method evidence

A unified approach for static and runtime verification : framework and applications

Static verification of software is becoming ever more effective and efficient. Still, static techniques either have high precision, in which case powerful judgements are hard to achieve automatically, or they use abstractions supporting increased automation, but possibly losing important aspects of the concrete system in the process. Runtime verification has complementary strengths and weaknesses. It combines full precision of the model (including the real deployment environment) with full automation, but cannot judge future and alternative runs. Another drawback of runtime verification can be the computational overhead of monitoring the running system which, although typically not very high, can still be prohibitive in certain settings. In this paper we propose a framework to combine static analysis techniques and runtime verification with the aim of getting the best of both techniques. In particular, we discuss an instantiation of our framework for the deductive theorem prover KeY, and the runtime verification tool Larva. Apart from combining static and dynamic verification, this approach also combines the data centric analysis of KeY with the control centric analysis of Larva. An advantage of the approach is that, through the use of a single specification which can be used by both analysis techniques, expensive parts of the analysis could be moved to the static phase, allowing the runtime monitor to make significant assumptions, dropping parts of expensive checks at runtime. We also discuss specific applications of our approach.peer-reviewe