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Accepted Paper:
Paper short abstract:
I bring insights from working with Refugee-Led Organizations, focusing on ethical, participatory data collection. I aim to explore how lived experiences shape impactful research, challenge power dynamics, and bridge community-driven data with evidence-based policy in humanitarian contexts
Paper long abstract:
Title: Data, Power, and Inclusion: Perspectives from Refugee-Led Research
This paper explores the production and use of “good data” in humanitarian and development contexts, focusing on the role of Refugee-Led Organizations (RLOs). By employing field researchers from refugee communities, RLOs ensure that the lived experiences of displaced individuals shape data collection and analysis, challenging traditional metrics of scientific rigor and promoting more inclusive research practices that reflect the realities of displacement.
A key aspect of this paper is the ethical engagement of research participants. Compensating interviewees through incentives acknowledges their time, effort, and potential financial costs, such as transportation or lost income. This practice underscores a commitment to equitable partnerships and enhances the integrity of data production by recognizing the contributions of community members in a tangible way.
The paper also examines how RLOs navigate complex power dynamics in producing data that is relevant, context-specific, and impactful for the communities they serve. By prioritizing community-based research, RLOs challenge traditional top-down research models, redefining concepts of accountability, power relations, and scientific rigor in data collection.
In addressing the limitations of conventional metrics, the paper explores how refugee-led research can inform more effective, contextually appropriate interventions, reshaping how evidence is used in humanitarian action and policy-making. Ultimately, it advocates for a more inclusive, ethical, and participatory approach to data generation in humanitarian and development work.
Making an impact: ethnographic approaches to producing “good data”
Session 1