First Advisor

Craig Shinn

Term of Graduation

January 2023

Date of Publication


Document Type





complex adaptive system, digital humanities, institutional field, K-12 education, topology, value

Physical Description

1 online resource ( pages)


Institutional fields serve as foundational bedrocks that shape and govern behaviors, norms, and practices within distinct domains of societal and organizational interactions. The emergence of machine learning and the ability to manipulate large datasets offer researchers and decision makers the potential ability to model and visualize the behavior associated with institutional fields.

This proof of concept provides an example of visualizing the changing conditions in the institutional field of public K-12 education in America as a topology. By interweaving three primary strands of theory – institutional fields, complexity in the guise of complex adaptive systems as a paradigm, and paradigms as logical systems, this research develops a novel methodology utilizing digital machine learning tools to generate a visualization of the institutional field.

The significant contribution of this study to institutional theory is the establishment of a proof of concept that institutional fields can be rigorously defined, measured, and modeled to yield valuable insights for policy and decision-makers. To demonstrate this proof of concept, the research introduces an Adaptive Institutional Topology Theory (AITT) as a guiding framework. This novel approach combines topic modeling and sentiment analysis with a newly developed Narrative Value-Based Coding (NVBC) technique, specifically designed to augment these digital methods.

Leveraging this model, it becomes possible to identify short-term trajectories for the institutional field, highlighting the roles of theory, definition, and visualization. Policymakers, organizational strategists, and researchers will find this methodology valuable in understanding institutional behavior, anticipating changes, and formulating effective strategies within the field. Importantly, the methodology presented is scale- independent, making it applicable across various scales of social organization.


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Available for download on Saturday, February 01, 2025