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State and federal governments use governance platforms to achieve central policy goals through distributed action at the local level. For example, California’s 2014 Sustainable Groundwater Management Act (SGMA) mandates local policy actors to work together to create new groundwater management institutions and plans. We argue that governance platforms entail a principal-agent problem where local decisions may deviate from central goals. We apply this argument to SGMA implementation, where local plans may respond more to local political economic conditions rather than address the groundwater problems prioritized by the state. Using a Structured Topic Model (STM) to analyze the content of 117 basin management plans, we regress each plan’s focus on core management reform priorities on local socio-economic and social-ecological indicators expected to shape how different communities respond to state requirements. Our results suggest that the focus of local plans diverges from problem conditions on issues like environmental justice and drinking water quality. This highlights how principal-agent logics of divergent preferences and information asymmetry can affect the design and implementation of governance platforms. 

Abstract This paper demonstrates an automated workflow for extracting network data from policy documents. We use natural language processing tools, part‐of‐speech tagging, and syntactic dependency parsing, to represent relationships between real‐world entities based on how they are described in text. Using a corpus of regional groundwater management plans, we demonstrate unique graph motifs created through parsing syntactic relationships and how document‐level syntax can be aggregated to develop large‐scale graphs. This approach complements and extends existing methods in public management and governance research by (1) expanding the feasible geographic and temporal scope of data collection and (2) allowing for customized representations of governance systems to fit different research applications, particularly by creating graphs with many different node and edge types. We conclude by reflecting on the challenges, limitations, and future directions of automated, text‐based methods for governance research. 

Abstract In this article, we respond to a critique of our earlier work examining the USDA Forest Service’s (USFS’s) planning processes. We appreciate that our critics introduce new data to the discussion of USFS planning. Further data integration is a promising path to developing a deeper understanding of agency activities. Our critics’ analysis largely supports our original claims. Our most important difference is in our conceptualization of the planning process’s relationship to agency goals. Although our critics conceive of the USFS’s legally prescribed planning processes as a barrier to land management activities, we believe that public comment periods, scientific analysis, and land management activities are tools the agency uses to achieve its goals of managing land in the public interest. Study Implications: The USDA Forest Service’s current planning process has been critiqued as a barrier to accomplishing land management activities, but it is also an important tool for insuring science-based management and understanding public values and interests that the agency is legally bound to uphold. 

Abstract Research on political control over government bureaucracy has primarily focused on direct exercises of power such as appointments, funding, agency design, and procedural rules. In this analysis, we extend this literature to consider politicians who leverage their institutional standing to influence the decisions of local field officials over whom they have no explicit authority. Using the case of the US Forest Service (USFS), we investigate whether field-level decisions are associated with the political preferences of individual congressional representatives. Our sample encompasses 7,681 resource extraction actions initiated and analyzed by 107 USFS field offices between 2005 and 2018. Using hierarchical Bayesian regression, we show that under periods of economic growth and stability, field offices situated in the districts of congressional representatives who oppose environmental regulation initiate more extractive actions (timber harvest, oil and gas drilling, grazing) and conduct less rigorous environmental reviews than field offices in the districts of representatives who favor environmental regulation. By extending existing theories about interactions between politicians and bureaucrats to consider informal means of influence, this work speaks to (1) the role of local political interests in shaping agency-wide policy outcomes and (2) the importance of considering informal and implicit means of influence that operate in concert with explicit control mechanisms to shape bureaucratic behavior. 

As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions. 

Abstract Abstract This paper draws on systematic data from the US Forest Service’s (USFS) Planning, Appeals and Litigation System to analyze how the agency conducts environmental impact assessments under the National Environmental Policy Act (NEPA). We find that only 1.9 percent of the 33,976 USFS decisions between 2005 and 2018 were processed as Environmental Impact Statements, the most rigorous and time-consuming level of analysis, whereas 82.3 percent of projects fit categorical exclusions. The median time to complete a NEPA analysis was 131 days. The number of new projects has declined dramatically in this period, with the USFS now initiating less than half as many projects per year as it did prior to 2010. We find substantial variation between USFS units in the number of projects completed and time to completion, with some units completing projects in half the time of others. These findings point toward avenues for improving the agency’s NEPA processes.