Autoethnographic assessment of a manifesto for more trustworthy, relevant, and just models.

Modelers are proposing sets of “better” practices to improve modeling processes and outcomes. We need to evaluate how they perform in practice. I use autoethnography to describe four of my modeling and interdisciplinary training experiences and test how I applied a specific set of modeling best practices (proposed in Eitzel 2021), exploring whether/how they resulted in processes whose outcomes were more relevant, trustworthy, and just than they would otherwise have been. The practices did improve the outcomes of my models, especially triangulating between multiple data sources and perspectives, improving transparency through better descriptions of methods and data, and engaging in community-based modeling. Some practices mutually reinforced each other, though balancing transparency with data sovereignty was critical when working with Indigenous communities. I invite other modelers to follow this example, analyzing their own experiences using autoethnography, testing my definitions of “better” modeling and proposed practices, or substituting their own.