Caring for Cultural Studies

This essay both inaugurates the editorship of Ted Striphas and reflects on the contributions of Cultural Studies’ longtime editor, Lawrence Grossberg. It opens by reflecting briefly on the journal's transformations over the last 28 years before taking stock of the wellbeing of Cultural Studies, the field. The claim here is that Cultural Studies devotes less time and attention than it used to in thinking through first principles, and thus that the future of the field hinges, in part, on asking whether core concepts are up to the task of answering to the urgent political questions of our time. The essay then recontextualizes the concept of culture, specifically with respect to the much-maligned (indeed, deservedly-maligned) ‘high culture’ tradition. In its recesses the author identifies a fugitive theoretical line in which culture signifies care, as opposed to subordination to patrician interests and aesthetic styles. This conceptual move sets the stage for the central argument of the essay, namely, that for Cultural Studies to live up to both its name and intellectual-political ambitions, it must devote significant time to caring for the infrastructure that sustains its body of ideas. The piece concludes by highlighting some of Lawrence Grossberg's efforts, as an infrastructuralist, to care for Cultural Studies and by observing how, moving forward, neither the field nor this journal will take care of itself.     

Childhood Maltreatment,Bullying Victimization,and Psychological Distress Among Gay and Bisexual Men

Sexual minority men report higher psychological distress than heterosexual men, including depression and anxiety. Research suggests that these health disparities may be due to the heightened stressors that gay, lesbian, and bisexual individuals experience. Some of these stressors occur early on in life, such as childhood abuse and bullying, and may include stressors that are topically related to sexual minority status, such as anti-gay bullying and teasing for gender nonconformity to masculine gender norms. We tested a structural equation model on the association between negative childhood experiences and adult psychological distress among 304 gay and bisexual men. The model fit the data well, and demonstrated an indirect effect of negative childhood experiences on adult psychological distress via dysfunctional thoughts toward oneself. The results integrate the childhood abuse and anti-gay bullying victimization literatures by showing that both forms of adverse childhood experiences are associated with adult psychological distress. The findings suggest the benefit of treatments to reduce negative, dysfunctional thoughts among gay and bisexual men who have experienced adverse childhood events.     

The interplay of spatial diffusion and marital assimilation of Mexicans in the United States, 1980–2011

Recent trends suggest a decline in the rate of intermarriage between Mexicans and non-Hispanic whites. In this paper, we argue that interpretations of this trend as a decline in preferences for intermarriage are misleading because of the lack of adequate data that captures both spatial and temporal variation in the level of intergroup contact. Using data from the Decennial Census (1980–2000) and the American Community Survey (2008–2011), we employ a novel methodological approach to disentangle the impact of spatial diffusion, ethnic replenishment, and shifts in preferences for homophily on Mexican ethnic intermarriage patterns across 543 Consistent Public Use Microdata Areas (c-PUMA). Once changes in the demographic composition of c-PUMAs are accounted for, multilevel models for repeated cross-sectional data provide no evidence of a change in the marital preferences of Mexicans over time. Trends in intermarriage rates are predominantly explained by compositional and structural changes.     

Willingness of land operators to participate in government-sponsored soil erosion control programs

Data were collected in 1986 from 552 landowners who were operating farms in erosion-prone areas of six Ohio counties. The purpose of the study was to identify predictive factors associated with willingness to participate in two types of soil conservation programs. Willingness to participate in an interest buy-down program was assessed using a three item index which evaluated support for the proposed conservation program. Willingness to sell private row-cropping rights to the federal government for permanent retirement of erosive land from agricultural production was also assessed. Multivariate regression analyses were used to examine the merits of a diffusion-farm structure perspective developed to guide the study. The findings revealed that 21.9% of the variance in the buy-down index and 21.5% of the variance in the purchase of the row-cropping rights factor were explained by the variables included in the model. The findings were basically consistent with theoretical expectations. Diffusion-type variables were shown to be the best predictors of willingness to participate in the government programs assessed.     

A STOCHASTIC MODEL FOR ANALYSIS OF VARIATIONS IN AIR POLLUTION LEVELS

Examination of environmental data seems to suggest that variations in air pollution levels from one point in time to another tend to follow some form of observable pattern. In this article, the authors explore an approach to observing, describing and analyzing these variations. They develop a model which is essentially a Markov framework and use it to suggest a number of interesting questions and to facilitate their solution. In this demonstration, the focus is on a single component of air pollution, suspended particulate matter. For implementation, a model would need to incorporate other elements of the pollution mix. As the composition of air pollution differs from one air shed to another, so will the parameters of the model.     

Regression and correlation for 3 × 3 rotation matrices

This paper investigates a regression model for orthogonal matrices introduced by Prentice (1989). It focuses on the special case of 3 × 3 rotation matrices. The model under study expresses the dependent rotation matrix V as A₁UA^t₂ perturbed by experimental errors, where A₁ and A₂ are unknown 3 × 3 rotation matrices and U is an explanatory 3 × 3 rotation matrix. Several specifications for the errors in this regression model are proposed. The asymptotic distributions, as the sample size n becomes large or as the experimental errors become small, of the least squares estimators for A₁ and A₂ are derived. A new algorithm for calculating the least squares estimates of A₁ and A₂ is presented. The independence model is not a submodel of Prentice's regression model, thus the independence between the U and the V sample cannot be tested when fitting Prentice's model. To overcome this difficulty, permutation tests of independence are investigated. Examples dealing with postural variations of subjects performing a drilling task and with the calibration of a camera system for motion analysis using a magnetic tracking device illustrate the methodology of this paper.     

Effects of Commitment and Psychological Centrality on Fathering

Propositions from identity theory suggest that interactional and affective commitment to a role identity affects the psychological centrality of that role identity. In turn, the centrality of one's role identity translates into role performance ( Stryker & Serpe, 1994 ). This conceptual model was tested with a sample of 186 fathers in first marriages with at least one child 18 years or younger. The results showed that fathers who perceived their wives as evaluating them positively as fathers were more likely to report higher levels of involvement in child‐related activities and place greater importance on the father role identity. This prominence, in turn, was associated with higher levels of involvement.     

Trade-Offs in the Labor Union-CEO Compensation Relationship

We examine the relationship between union presence and CEO compensation. Based on the notion that the union taxes the firm's returns, we test whether unions successfully capture a larger share of the firm's returns with respect to the share captured by the CEO. On average CEOs in union firms are paid 19 percent less than those in nonunion firms, after controlling for several determinants of CEO pay. CEO compensation risk is lower in union firms because the reduced compensation risk is a trade-off for the lower level of CEO compensation.     

Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization

Hospital readmissions present an increasingly important challenge for health‐care organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the United States alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post‐discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post‐discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay‐time models into a weakly coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi‐methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40–70% of readmissions before they generate an emergency readmission.