This dissertation investigates inequality in community-based mental health services by comparing public safety net and elite private treatment in Los Angeles. Drawing on more than four years of ethnographic fieldwork, interviews, and historical research, the dissertation analyzes the way social class shapes the care, control, and empowerment of people diagnosed with serious psychiatric disabilities. A substantial literature has investigated the consequences of deinstitutionalization, such as the crisis of criminalization and homelessness for poor psychiatric services users, yet far less is known about the experience of those with more social and economic resources. Comparing clinics for the poor and rich, I show how the very meaning of treatment shifts by class context. In a public safety net setting oriented to urban poverty governance, staff members work to secure their clientele access to survival resources. With few therapeutic interventions beyond psychiatric medication, care is oriented to housing and basic stability. Providers engage in what I call a “tolerant containment” approach that accepts a surprising amount of seemingly problematic behavior, like drug use and “non-compliance,” with intervention focused on harm reduction and creative solutions for keeping people off the streets. In the elite private settings, on the other hand, providers have access to a broad array of therapeutic interventions and a goal of transformative care. Far from letting people choose to be “deviant,” I find providers heavily invest therapeutic resources to both cultivate and control privileged patients. Building on sociological theories of privileged childrearing, I term this form of family-driven governance “concerted constraint.” Holding these cases as mirrors to one another, I argue that the clinics are not simply treating mental illness, but also attempting to create different kinds of people for their respective classed milieus.

The peer-review system of most academic conferences relies on the anonymity of both the authors and the reviewers of submissions. In particular with respect to the authors, the anonymity requirement is heavily disputed and pros and cons are discussed exclusively on a qualitative level.

In this paper, we contribute a quantitative argument to this discussion by showing that it is possible for a machine to reveal the identity of authors of scientific publications with high accuracy. We attack the anonymity of authors using statistical analysis of multiple heterogeneous aspects of a paper, such as its citations, its writing style, and its content. We apply several multi-label, multi-class machine learning methods to model the patterns exhibited in each feature category for individual authors and combine them to a single ensemble classifier to deanonymize authors with high accuracy. To the best of our knowledge, this is the first approach that exploits multiple categories of discriminative features and uses multiple, partially complementing classifiers in a single, focused attack on the anonymity of the authors of an academic publication.

We evaluate our author identification framework, deAnon, based on a real-world data set of 3,894 papers. From these papers, we target 1,405 productive authors that each have at least 3 publications in our data set. Our approach returns a ranking of probable authors for anonymous papers, an ordering for guessing the authors of a paper. In our experiments, following this ranking, the first guess corresponds to one of the authors of a paper in 39.7% of the cases, and at least one of the authors is among the top 10 guesses in 65.6% of all cases. Thus, deAnon significantly outperforms current state-of-the-art techniques for automatic deanonymization.