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We combine emerging computational text analysis approaches with traditional methods of quantitative social science data analysis to describe patterns of education policy development and diffusion over a nine-year period among a sample of 870 California school districts. In particular, we focus on school districts’ policies for using special allocations of state funding to increase equitable outcomes among low-income, English-learner, and foster youth students. Using topic modeling and text network mapping, we trace the emergence and over-time evolution of policy communities: clusters of school districts that articulate similar policy approaches to increasing educational equity. We describe the axes of topical similarity that define these policy communities as well as the district-level predictors of policy community co-membership, describing the policy landscape’s shifting contours and the contextual and organizational correlates of mimetic isomorphism among public school districts.