Bolstering superficial measurement robustness with community-based data foundations

Robustness analysis is a methodological process of identifying results that converge over a variety of independent identifications, models, measurements, or derivations. In this discussion, we focus on measurement robustness, where convergent results are obtained over different measurement methods, indicating reliable detection. Our aim is to identify a methodological problem with convergent results applicable to measurement practice in inequitable social contexts. We argue that even under ideal function of measurement robustness, there is still a deeper methodological problem about measurement choices and strategies: the 'sacrifice of representational adequacy for generality' (SRAG). We detail SRAG and then apply it using two case studies, where convergent measurements conceal pollution masking and pollution burden. Finally, we offer a solution to SRAG through the analysis of robust community-based data practices. By describing the community-led efforts behind Shingle Mountain and the Joppa Environmental Health Project, we illustrate how an effective cross-checking structure can correct measurement goals and strategies, thereby, promoting representational adequacy.