Racial capitalism and STEM education in Chicago Public Schools

ABSTRACT

This article analyzes the role of STEM initiatives designed by city and corporate elites in a large urban district and outlines an alternative, grassroots vision for (STEM) education and city schools. Within a neoliberal context of gentrification, displacement, disinvestment, and privatization, STEM schools have become strategic components of Chicago’s ‘portfolio district’ that serve the interests of racial capitalism in three ways. First, STEM schools provide a claim to fairness in the midst of racist school closures. Second, STEM high schools are a corporate strategy for racially stratified labor force preparation that restricts curriculum and reifies tracking. Third, curriculum restriction prioritizes corporate interests over students’ capacity to shape their communities and the world. The authors draw on the wisdom of Chicago communities who have led resistance against corporate education reform to critique Chicago STEM policy and point to critical mathematics and science education as part of a model for sustainable community schools.     

Identification Properties of Recent Production Function Estimators

This paper examines some of the recent literature on the estimation of production functions. We focus on techniques suggested in two recent papers, Olley and Pakes (1996) and Levinsohn and Petrin (2003). While there are some solid and intuitive identification ideas in these papers, we argue that the techniques can suffer from functional dependence problems. We suggest an alternative approach that is based on the ideas in these papers, but does not suffer from the functional dependence problems and produces consistent estimates under alternative data generating processes for which the original procedures do not.     

Robust Bayesian model selection for variable clustering with the Gaussian graphical model

Statistics and Computing - Variable clustering is important for explanatory analysis. However, only few dedicated methods for variable clustering with the Gaussian graphical model have been...     

Linear Increments with Non‐monotone Missing Data and Measurement Error

Linear increments (LI) are used to analyse repeated outcome data with missing values. Previously, two LI methods have been proposed, one allowing non‐monotone missingness but not independent measurement error and one allowing independent measurement error but only monotone missingness. In both, it was suggested that the expected increment could depend on current outcome. We show that LI can allow non‐monotone missingness and either independent measurement error of unknown variance or dependence of expected increment on current outcome but not both. A popular alternative to LI is a multivariate normal model ignoring the missingness pattern. This gives consistent estimation when data are normally distributed and missing at random (MAR). We clarify the relation between MAR and the assumptions of LI and show that for continuous outcomes multivariate normal estimators are also consistent under (non‐MAR and non‐normal) assumptions not much stronger than those of LI. Moreover, when missingness is non‐monotone, they are typically more efficient.     

Semiparametric Estimation of a Change-point for Recurrent Events Data

One particular recurrent events data scenario involves patients experiencing events according to a common intensity rate, and then a treatment may be applied. The treatment might be effective for a limited amount of time, so that the intensity rate would be expected to change abruptly when the effect of the treatment wears out. In particular, we allow models for the intensity rate, post-treatment, to be at first decreasing and then change to increasing (and vice versa). Two estimators of the location of this change are proposed.     

Developing and testing a satisfaction survey for nursing home residents: the Ohio Experience

Input from consumers has become an important part of quality improvement in long-term care and for consumer decision-making. This paper documents the development of the Ohio Nursing Home Resident Satisfaction Survey (ONHRSS) through a partnership of state government, research, and industry experts. The instrument was tested and refined through two waves of data--a pretest phase and later with statewide data. Exploratory and confirmatory analyses with statewide data identified eight primary factors along with an underlying, secondary Global Satisfaction factor. Reliability of the domains ranged from .69 to .95. Recommendations for further refinement and testing of the instrument are discussed along with policy and practice implications.     

Vulnerabilities and risks in population and environment studies

In the study of risks , different sciences use the same category in different ways, each related to its own ontological assumptions. But many of these fields communicate very little with one another. This article seeks to approximate two of these areas of study that have shown similar concerns and that can mutually strengthen one another, namely, geography and demography. Geography was one of the first disciplines to include risk in its environmental dimension and has had broad experience in simultaneously focusing on social and natural dynamics. Demography, on the other hand, faces greater difficulties because only recently has it incorporated the environmental dimension into its scientific scope. Both have brought the concept of vulnerability into their conceptual framework as complementary to that of risk. Geographers understand vulnerability as a more symbiotic form of the relationship between society and nature, whereas demographers give it a strong socioeconomic component. In this regard, the conceptual discussion on risks and vulnerabilities, in its attempt at approximating these two fields, is a way of conceptually advancing and strengthening the different approaches to empirical work, especially in population–environment studies which is the common ground for the dialogue between the two disciplines.