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The growing interest in systems-change initiatives sits alongside increasing pressure to demonstrate value for money (VFM), which is challenging for emergent, interconnected and often intangible work. A new way to assess the VFM of systems-change work involves considering the value of changing system conditions and the creation of potential value for future systems transformation. This innovation combines the Water of Systems Change framework with the Cycles of Value Creation to create five...
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This blog by Jonathan Kuhn-Patrick (UK Evaluation Society Trustee and AI Working Group Lead) explores the five emotional stages many evaluators experience as they begin working with AI.
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Quality assurance strategies when working on unfamiliar topics
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Amid growing concerns over sustainable development failures, scholars are exploring the ‘regenerative paradigm’ as a pathway for systemic change; yet, its paradigmatic foundations remain underexamined. Using thematic analysis, we analyse the regenerative knowledge field through an integrative review of 320 cross-disciplinary articles on regenerative approaches, synthesising findings into an interactive Regenerative Paradigm Map with 7 principles, 33 themes, and 253 specific elements. We...
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This practical guide from the UK Evaluation Society explores how evaluators can use AI tools responsibly and transparently across all stages of the evaluation process.
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Track to Change homepage
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Earlier this year we spoke to 102 leaders from across the globe to ask: Ten years from now, what will we regret not having done today? This framing turned out to be a powerful device for collective horizon scanning and foresight. Across every conversation there was a sense that this moment demands more from us all, along with a strong eagerness to explore what we can own and act on. More than that, these discussions surfaced direction, hope and a way forward. From CEOs, professors, PhDs,...
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In a world where social, ecological, health, and other problems are influenced by multiple, intersecting systems, why do most interventions target single-factor solutions? Why does so much evaluation settle for incremental change, and how could it contribute instead to deeper, lasting, transformative change? Authors Emily F. Gates and Pablo Vidueira make two interconnected arguments in this book: they critique the "fixed" approach of traditional program evaluation and policy analysis, and...
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This paper shares an insider's perspective on the spread of Thinking and Working Politically (TWP) across the US Agency for International Development (USAID), highlighting lessons learned along the way that are relevant even beyond the dissolution of the Agency in 2025.
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Critical summary – the single through‑line of the essay
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Cooke and Kothari argue that the seductive claims of participation often provide cover for shallow and tokenistic development practices that fail to address unequal power relationships. This paper provides a novel mechanism that documents and enables a three-dimensional analysis of (a) who participates (b) at which project stages and (c) at what level of agency and power. No other mechanism was found in the literature for systematically tracing and analysing changes in power relationship...
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Despite an emerging body of scholarship on applying generative AI (GenAI) to qualitative data analysis, this area remains underdeveloped. This article evaluates how GenAI can support thematic analysis using a publicly available interview dataset from Lumivero. It introduces Guided AI Thematic Analysis (GAITA), an adaptation of King et al.’s (2018) Template Analysis. This framework positions researchers as a reflexive instrument and intellectual leader while thoroughly guiding GPT-4 in four...
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The CGIAR 2030 Research and Innovation Strategy commits organizational change with seven ways of working, including “Making the digital revolution central to our way of working”. In that context, Artificial Intelligence (AI), introduces both opportunities and risks to evaluation practice. Guided by the CGIAR-wide Evaluation Framework, integrating AI tools requires a governance approach to balance innovation with ethical responsibility, ensuring transparency, fairness, accountability, and...
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Today’s critical challenges are complex and require system level change. Issues like ending poverty, reversing biodiversity collapse, creating more equitable and sustainable food systems, or ending health inequalities, will not respond to neat siloed technical interventions. These are not problems we can tackle head on. Rather, we need to change the conditions (systems) that create these problems. Consequently, many of the most ambitious philanthropic foundations are adopting ‘systems...
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One of the main questions evaluators have when using AI is: "How can I know if it performs well?" To answer this question, we have developed a guidance note aimed at capturing what we have learned so far about integrating AI into evaluation and to offer a framework for further exploration. Since we began our experiments with Large Language Models (LLMs) in the Spring of 2023, we have made significant progress in using Generative Artificial Intelligence (GenAI) for processing and analyzing...
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Hey there 👋We've created a new presentation called "Questions you can answer with causal mapping." It's designed to help you get a better handle on what causal mapping is all abou
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Process tracing (PT) involves a detailed analysis of the processes that link interventions to outcomes. PT is particularly useful for evaluating interventions that are difficult to quantify, such as knowledge work or institution building. It involves creating a detailed causal theory, tracking the process theory of change (pToC) by examining the observable evidence, and learning general lessons from the cases studied. PT offers two main benefits to evaluators: first, it provides a clear way...
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This article presents an artificial intelligence-assisted causal mapping pipeline for gathering and analysing stakeholder perspectives at scale. Evidence relevant to constructing a programme theory, as well as evidence for the causal influences flowing through it, are both collected at the same time, without the evaluator needing to possess a prior theory. The method uses an artificial intelligence interviewer to conduct interviews, automated coding to identify causal claims in the...
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