Centering Attention Where It Counts: A Collaborative Evaluation Model for Meaningful Use of Evidence

In collaborative evaluations, especially those shaped by equity concerns and limited resources, it is essential to focus attention where it matters most. This session presents a practical application of an Attention-Based Theory of Change (ToC), reimagined within the framework of collaborative evaluation. Drawing inspiration from how attention functions in both human decision-making and AI models, this approach integrates three principles—relevance scoring, weighted focus, and contextual understanding—to help evaluation teams and stakeholders co-determine what requires shared focus. This model is grounded in the six components of the Model for Collaborative Evaluations (MCE) and provides a structure for aligning attention with stakeholder priorities. It is particularly suited for complex or data-heavy contexts where evaluators must prioritize both process and use. Participants will explore how this model supports ethical decision-making, clarity, and co-ownership. The session contributes to the AEA 2025 theme by highlighting attention as a shared, strategic act in collaborative evaluation.

Michael Harnar; Michelle Rincones-Rodriguez; liliana Rodríguez-Campos