Autoethnographic assessment of a manifesto for more trustworthy, relevant, and just models. (June 2023)

• Record Type: Journal Article
• Authors: Eitzel, M.V.
• Is Part Of: Environmental modelling & software. Volume 164(2023)
• Publication Date: 2023-06
• Subjects: Moderate autoethnography -- Data science best practices -- Community-based modeling -- Correction of published research -- Interdisciplinary training -- Science and technology studies; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105690
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Graphical abstract: Highlights: Autoethnography is an effective qualitative method to assess modeling best practices. Trustworthiness and transparency in modeling can be enhanced with 'data biographies'. Triangulating between datasets and different communities gives better model results. Engaging with communities impacted by models improves modeling relevance and justice. Transparency and justice practices can work together but must respect sovereignty.