Who will man the rigs when we go?” transnational demographic fever dreams between Qatar and Texas

The educational project of producing engineers in Qatar is uniquely embedded in global capitalism, particularly as a field closely tied to the development of the oil and gas industry, the military and logistics spaces across the Gulf. Over the past two decades, U.S. universities based in the region have become significant spaces where new generations of managerial engineering labor are educated. Drawing on 18 months of institutional ethnographic research, I examine Texas A&M University at Qatar’s (TAMUQ) role in managing the gender demographics of Qatari engineering labor and the experiences of students navigating these institutional mechanisms. The increasing number of women studying at Texas A&M’s engineering branch campus are publicly celebrated by the university as the embodiment of progress in Qatar. At the same time, TAMUQ has worked to mitigate the feminization of engineering through outreach activities that present engineering as a masculine patriotic endeavor. To unpack these contradictory tendencies, I build on the feminist concept of “demographic fever dreams.” Through an examination of contradictory population-based anxieties about Qatari engineering students, I argue that a U.S. land-grant university is a participant and driver of fantasies and fears regarding the future of racialized and gendered labor hierarchies and fossil-fueled capitalism in the Gulf. In doing so, this article offers a grounded feminist intervention to examine the connections between transnational education, U.S. hegemony, and the fossil fuel industry.

Comparing the Borich model with the Ranked Discrepancy Model for competency assessment: A novel approach

In 1980, Borich presented a new model that allowed errors in an individual’s judgment of self-proficiency to be offset by considering the perception of a group. The model relied upon the calculation of means for competency items measured with ordinal scales, an approach subject to debate in modern times. The purpose of our study was to explore the use of a novel approach we developed, the Ranked Discrepancy Model (RDM), as an alternative method to the Borich model for determining training needs. Data obtained from an online survey of extension professionals employed by a land-grant university in the United States was used to compare the training needs identified by applying the Borich model with those identified by applying the RDM. A very strong and statistically significant correlation existed between the scores derived from using both models, demonstrating a high level of consistency between models. Researchers conducting competency research should consider adopting the RDM given its suitability for delivering results that closely resemble findings from the Borich model while providing improved rigor in methods and increased detail about training needs.

Everything Is Faster: How Do Land-Grant University–Based Plant Diagnostic Laboratories Keep Up with a Rapidly Changing World?

Plant diagnostic laboratories (PDLs) are at the heart of land-grant universities (LGUs) and their extension mission to connect citizens with research-based information. Although research and technological advances have led to many modern methods and technologies in plant pathology diagnostics, the pace of adopting those methods into services at PDLs has many complexities we aim to explore in this review. We seek to identify current challenges in plant disease diagnostics, as well as diagnosticians' and administrators'perceptions of PDLs' many roles. Surveys of diagnosticians and administrators were conducted to understand the current climate on these topics. We hope this article reaches researchers developing diagnostic methods with modern and new technologies to foster a better understanding of PDL diagnosticians’ perspective on method implementation. Ultimately, increasing researchers’ awareness of the factors influencing method adoption by PDLs encourages support, collaboration, and partnerships to advance plant diagnostics.

Forward Momentum: Providing Supportive Space for EdD Students’ Dissertation Progression through Weekly Online Writing Sessions

This essay describes an online writing group introduced to a CPED EdD program at a research-intensive, land-grant university during the summer of 2020 when the existing face-to-face program shifted to fully online delivery. The purpose of the writing sessions was to support EdD student practitioners with dissertation writing productivity by offering multiple weekly opportunities for synchronous writing sessions via Zoom online video conferencing. Although this new program is still in development, initial student feedback suggests that the writing sessions not only supported students’ dissertation progression, but it also established a sense of community and social support in an online environment. Lessons learned are shared, and we argue that this could be an ideal time to offer online writing sessions, especially since the pandemic will continue into the coming months.

Expectations and outcomes: How technologies drive virtual teaching

We evaluate the effectiveness of using Zoom for learning Modern Physics topics in a large engineering physics class at a land-grant university in Texas. This virtual technology challenged both students and professors. By implementing different approaches: providing ahead of the lectures reading assignments, PowerPoint presentations, and pre-recorded videos, during the class administering attendance mini-quizzes, and afterward, the assigning on WebAssign homework, elements of an effective class setting were introduced. Passion and mutual understanding united students and teachers to be stronger than the Covid-19 pandemic and to endure the challenging teaching environment. We describe some features of the technologies and the learning expectations. The evaluation of outcomes was done in two ways: quantitatively, by statistical measures, and qualitatively, through an anonymous student survey and a university-wide teacher evaluation.

Evaluating the utility of high-resolution proximity metrics in predicting the spread of COVID-19

High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such a mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ODE based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We subsequently evaluate the metrics' utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and 87% F1-score.