Governmentality in higher education : a critical analysis of the National Survey of Student Engagement (NSSE)

In this dissertation, the National Survey of Student Engagement (NSSE) serves as a nodal point through which to examine the power relations shaping the direction and practices of higher education in the twenty-first century. Theoretically, my analysis is informed by Foucault’s concept of governmentality, briefly defined as a technology of power that influences or shapes behavior from a distance. This form of governance operates through apparatuses of security, which include higher education. Foucault identified three essential characteristics of an apparatus—the market, the milieu, and the processes of normalization—through which administrative mechanisms and practices operate and govern populations. In this project, my primary focus is on the governance of faculty and administrators, as a population, at residential colleges and universities. I argue that the existing milieu of accountability is one dominated by the neoliberal assumption that all activity—including higher education—works best when governed by market forces alone, reducing higher education to a market-mediated private good. Under these conditions, what many in the academy believe is an essential purpose of higher education—to educate students broadly, to contribute knowledge for the public good, and to serve as society’s critic and social conscience (Washburn 227)—is being eroded. Although NSSE emerged as a form of resistance to commercial college rankings, it did not challenge the forces that empowered the rankings in the first place. Indeed, NSSE data are now being used to make institutions even more responsive to market forces. Furthermore, NSSE’s use has a normalizing effect that tends to homogenize classroom practices and erode the autonomy of faculty in the educational process. It also positions students as part of the system of surveillance. In the end, if aspects of higher education that are essential to maintaining a civil society are left to be defined solely in market terms, the result may be a less vibrant and, ultimately, a less just society.

Analysis of spring break-up and its effects on a biomass feedstock supply chain in northern Michigan

Demand for bio-fuels is expected to increase, due to rising prices of fossil fuels and concerns over greenhouse gas emissions and energy security. The overall cost of biomass energy generation is primarily related to biomass harvesting activity, transportation, and storage. With a commercial-scale cellulosic ethanol processing facility in Kinross Township of Chippewa County, Michigan about to be built, models including a simulation model and an optimization model have been developed to provide decision support for the facility. Both models track cost, emissions and energy consumption. While the optimization model provides guidance for a long-term strategic plan, the simulation model aims to present detailed output for specified operational scenarios over an annual period. Most importantly, the simulation model considers the uncertainty of spring break-up timing, i.e., seasonal road restrictions. Spring break-up timing is important because it will impact the feasibility of harvesting activity and the time duration of transportation restrictions, which significantly changes the availability of feedstock for the processing facility. This thesis focuses on the statistical model of spring break-up used in the simulation model. Spring break-up timing depends on various factors, including temperature, road conditions and soil type, as well as individual decision making processes at the county level. The spring break-up model, based on the historical spring break-up data from 27 counties over the period of 2002-2010, starts by specifying the probability distribution of a particular county’s spring break-up start day and end day, and then relates the spring break-up timing of the other counties in the harvesting zone to the first county. In order to estimate the dependence relationship between counties, regression analyses, including standard linear regression and reduced major axis regression, are conducted. Using realizations (scenarios) of spring break-up generated by the statistical spring breakup model, the simulation model is able to probabilistically evaluate different harvesting and transportation plans to help the bio-fuel facility select the most effective strategy. For early spring break-up, which usually indicates a longer than average break-up period, more log storage is required, total cost increases, and the probability of plant closure increases. The risk of plant closure may be partially offset through increased use of rail transportation, which is not subject to spring break-up restrictions. However, rail availability and rail yard storage may then become limiting factors in the supply chain. Rail use will impact total cost, energy consumption, system-wide CO2 emissions, and the reliability of providing feedstock to the bio-fuel processing facility.