Search

In an era of expanding global educational privatization and shifting policies on how to fund educational facilities in many states in the US, this study engages the lenses of critical policy analysis and fiscal sociology to examine educational privatization in the school facilities industry in California. Employing critical policy document analysis to examine approximately 40 primary and secondary source documents including propositions, bills, government and education codes, facilities reports, and state public debt data, this paper addresses the following research question: How have education finance policies shaped the system of school district facilities financing over time, specifically with regard to the field of private actors involved in the school facilities industry? This paper examines the historical policy context for privatization in school district facilities financing, evaluating how policies have evolved, been implemented, and affected stakeholders over time in the broader neoliberal context. Findings indicate that policies promoting privatization in education finance impact school districts’ abilities to provide equitable facilities to their students. Also, the complex and intermittent process of financing school facilities has facilitated the rise of specialized private actors in the school facilities industry that has now organized to engage in private-sector policy setting. [ABSTRACT FROM AUTHOR]

Predictive policing is a research field whose principal aim is to develop machines for predicting crimes, drawing on machine learning algorithms and the growing availability of a diversity of data. This paper deals with the case of the algorithm of PredPol, the best-known startup in predictive policing. The mathematicians behind it took their inspiration from an algorithm created by a French seismologist, a professor in earth sciences at the University of Savoie. As the source code of the PredPol platform is kept inaccessible as a trade secret, the author contacted the seismologist directly in order to try to understand the predictions of the company's algorithm. Using the same method of calculation on the same data, the seismologist arrived at a different, more cautious interpretation of the algorithm's capacity to predict crime. How were these predictive analyses formed on the two sides of the Atlantic? How do predictive algorithms come to exist differently in these different contexts? How and why is it that predictive machines can foretell a crime that is yet to be committed in a California laboratory, and yet no longer work in another laboratory in Chambéry? In answering these questions, I found that machine learning researchers have a moral vision of their own activity that can be understood by analyzing the values and material consequences involved in the evaluation tests that are used to create the predictions. [ABSTRACT FROM AUTHOR]