Building a Method: Challenges and Lessons from Developing an Impact Evaluation Framework for Public Investment in Open Source Software
Laia Domenech Burin
Abstract
Modern societies and institutions depend on an invisible layer of open digital infrastructure: foundational, openly accessible digital technologies that support a broad range of social and economic activities. Open source software is a core component of this infrastructure, underpinning everyday technologies and critical systems in various critical sectors. As open source software becomes increasingly recognised, funded, and governed as open digital infrastructure, policymakers face growing demands for accountability, transparency, and credible evidence of impact. However, methods for evaluating public investment in open source software remain underdeveloped, with no widely accepted methodological standard. This paper describes the Sovereign Tech Agency's transition from qualitative reporting and funding-recipient self-assessment towards a mixed-methods approach that incorporates quantitative, goal-oriented evaluation to inform funding decisions. Drawing on the development of this framework, it examines its initial design, the practical and structural challenges encountered, and the responses adopted. The work identifies three central challenges: evaluation indicators must reflect heterogeneous project goals and contexts; quantitative assessment relies on fragmented and fragile data infrastructure; and observed changes before, during, and after investment do not establish causal attribution without additional research designs. The paper argues that evaluating investment in open source software is not only a methodological task but also a governance practice, dependent on sustainable open research infrastructure, community knowledge, and ongoing methodological refinement.
Create a lesson
Related papers
U.S. State Guidance on AI in K-12 Education: A Comparative Content and Citation Analysis
Shahan Ali Memon, Anna-Maria Gueorguieva, Anukriti Kumar et al.
Using AI Predictions to Augment Rather Than Replace Surveys
Lai Wei, Yifei Zhu
Beyond the principle-practice gap: Responsible AI as situated epistemic and ethical practice
Thao Do Ngoc, Emma Carmel
Beyond accuracy: what AI assurance needs to establish in public services
Louise Yorke
Does Personality Persuade? Investigating Personality Trait Prompting and Persuasion in Large Language Models
Ruben Boone, Gilles Eerlings, Sebe Vanbrabant et al.
Who Gets Counted? Large Language Models, Informative Refusal, and Downstream Bias in Name-Based Demographic Classification
Christian Pipal, Morgan Wack, Eva-Maria Vogel