Many social and ecological problems require us to consider objectively verifiable phenomena as well as subjective states of knowledge and associated value systems. When approximating the facts of reality, the wisdom of crowds phenomenon demonstrates that many pooled estimates can be more accurate than individual or expert estimates. For complex and social systems, wisdom of crowd approaches are improved by aggregating knowledge over subpopulations. In this paper we consider subpopulations defined by different sets of shared values. We first discuss two approaches to qualitatively understanding differences in value sets held by individuals and groups, which in turn motivate our discussion of three unsupervised methods for identifying subpopulations based upon value-laden statements in narrative data from hyperlocal maternal and child health (MCH) contexts in Gombe State, Nigeria. We employ data science techniques and compare methods to assess the stability of inferences. We find the hypothesized groups to be method dependent and discuss implications for wisdom-of-crowd estimates in sustainable development contexts.

The replicability of research is crucial for building trust in the peer review process and transitioning knowledge to realworld applications. While manual peer review excels in some regards, the variability of reviewer expertise, publication requirements, and research domains brings about uncertainty in the process. Replicability, in particular, is not necessarily a priority; this is evidenced by repeated failures in replication attempts such as the Psychology Reproducibility Project, where 61 of 100 replications fail. Improving human comprehension of decisive factors is crucial for integrating automated systems for replicability prediction into the review process. We develop a robust, automated method for semantic parsing, information extraction, and replication prediction that operates directly on PDFs. We introduce features that have not been explored in prior work, construct argument structures to guide understanding, and provide preliminary results for replication prediction.