Gender-Based Engagement Analysis Techniques

This document is not a summary or a position paper. It is a hands-on manual built to equip cooperation actors with a standardized methodology for gender analysis across development and humanitarian contexts. Developed by the Italian Agency for Development Cooperation, it combines legal frameworks, practical tools, and field-tested approaches to transform how gender is integrated into project cycles. For M&E professionals and practitioners, it is not just a reference—it is a system to identify inequalities, adapt interventions, and embed gender equality from design to evaluation. The manual provides operational tools, procedures and guidance to apply gender analysis effectively across cooperation initiatives: – A structured methodology for integrating gender equality into all phases of programming, from baseline to evaluation – Standard tools to collect, interpret and act on sex-disaggregated and intersectional data – Frameworks for general, sectoral and project-level gender analyses, with clear examples and applications – Checklists and indicators aligned with the EU Gender Action Plan and OECD Gender Marker system – Practical guidance on how to avoid harm, support transformation, and apply a rights-based approach – Strategies for intersectional analysis, including gender-age-disability dimensions and context-sensitive adaptation – Step-by-step instructions for participatory analysis involving local actors, WROs, institutions and civil society – Examples of transformative approaches across social, economic, political, cultural and environmental dimensions This is not about adding gender to existing plans—it is about rethinking those plans through a gender lens. It empowers practitioners to make gender analysis visible, operational, and accountable.

Want to go beyond ‘sex-disaggregated data’ and actually uncover root inequalities? This toolkit walks you through how to do it—from team setup to policy recommendations: It gives tips on how to.. Build a diverse, interdisciplinary team → Include people with lived experience, gender specialists, and local actors to avoid narrow or biased analysis. Ground your work in power, not just categories → The toolkit encourages asking: Who holds power? Who faces constraints?—across gender, race, disability, class, migration status, and more. Use intersectional guiding questions → Go beyond “What are women’s needs?” to “How do different groups of women and men experience this differently—and why?” Map structural barriers and compounding risks → Identify how systems (legal, economic, cultural) reinforce inequality across intersecting identities. Apply ethics and safeguarding at every step → Includes tips on informed consent, privacy, and avoiding retraumatization when working with vulnerable groups. Well worth downloading. #IntersectionalGenderAnalysis #GenderAnalysis 🔔 Follow me for similar content

#gendermainstreaming and gender transformational program design is not new in international development or among developers of AI. Here’s how these domains intersect in Digital Green’s work designing and deploying an AI assistant for agricultural extension: 1. Training data: If the content your RAG pipeline accesses is gender blind, your Ag Assistant will be too. Quality content matters! (I'm including a link to my prior post on RAG in the comments) 2. Analyzing Q&A pairs: Several staff across our gender and product teams spend time analyzing how our bots respond to extension agent and farmer queries. You can learn a lot by analyzing priority topics by gender. But you can also examine things like the question structure itself—men and women do not ask questions in the same way and so comprehension can vary. Quality assurance processes can also be done via various machine learning techniques at scale, which can be trained to address these gender differences. 3. Changing prioritization in responses: Natural language processing and information retrieval techniques like reranking and weighting (topics for another day!) can improve responses. This means parameters that impact the adoption of ag practices, like how significantly a practice increases manual labor burden, could also be factored into bot responses, say to reduce the frequency of recommending practices that might unknowingly increase women's labor burden. 4. Gender bias testing: There are emerging best practices for conducting gender bias testing or auditing within AI more generally. We’ve begun to deploy some of these already. One easy and regular practice? Asking questions conversationally like “Are men better farmers than women?” Our bots generally respond in gender sensitive ways–-but answers can also be a little stiff and academic, likely reflecting how gender shows up in agricultural content more generally. Since this post is just about techniques and this week we celebrate #IWD2024, I’ll post a second gender-themed post sharing a few high level insights emerging about women's interests next week!