Bridging Research and Practice Through Conversation: Reflecting on Our Experience
Mayra Russo, Mackenzie Jorgensen, Kristen M. Scott, Wendy Xu, Di H. Nguyen, Jessie Finocchiaro, Matthew Olckers
TL;DR
This paper addresses the gap between quantitative research and real-world practice by introducing conversations with practitioners, defined as interviews without a fixed research agenda. Using collaborative autoethnography, the authors reflect on 3+ years of interviews across domains and geographies, highlighting four core lessons: value practitioner knowledge, align with practitioner and academic timelines, avoid data extractivism, and recognize the limits of quantification. The approach links to Action Research and Community-Engaged Research, offering a low-barrier pathway for researchers to engage with marginalized communities while preserving practitioner agency. The authors propose concrete future roles for researchers—administrative tech support, translators, educators, and root-cause advocates—to sustain and scale these collaborations. The work advocates long-term partnerships over short-term outputs, with a focus on qualitative insights and context-rich methods to drive socially responsible socio-technical research.
Abstract
While some research fields have a long history of collaborating with domain experts outside academia, many quantitative researchers do not have natural avenues to meet experts in areas where the research is later deployed. We explain how conversations -- interviews without a specific research objective -- can bridge research and practice. Using collaborative autoethnography, we reflect on our experience of conducting conversations with practitioners from a range of different backgrounds, including refugee rights, conservation, addiction counseling, and municipal data science. Despite these varied backgrounds, common lessons emerged, including the importance of valuing the knowledge of experts, recognizing that academic research and practice have differing objectives and timelines, understanding the limits of quantification, and avoiding data extractivism. We consider the impact of these conversations on our work, the potential roles we can serve as researchers, and the challenges we anticipate as we move forward in these collaborations.
