Debate for Civic Learning: A Model for Renewing Higher Education’s Civic Mission

Rapidly eroding financial support and tuition increases that outpace inflation threaten the viability of an education that considers civic engagement as foundational. Simultaneously, institutions of higher education are increasingly perceived by the public as market-driven entities existing for the economic benefit of the individual, the upward mobility of a social class, and in turn the further sedimentation of racial and class differences. Now, more than ever, our nation is in need of deliberate attempts to fashion common understandings, ways to navigate inevitable disagreements, and reasonable paths forward. Higher education is positioned to respond to these civic needs but requires a commitment to be bold and remain dedicated to our shared civic mission in the face of alarming polarization and vacated institutional trust. One way institutions of higher education can return to their shared sense of civic mission is with the integration of debate across the curriculum through innovative partnerships and collaborative design. Debate across the curriculum utilizes intentional course redesign to offer active learning experiences that combine public speaking, evidence-based reasoning, collaborative learning, and argumentation into various advocacy simulations. The debate for civic learning model has faculty partnered across multiple institutions to design, integrate, and assess debate-based pedagogy to positively impact student civic learning. Students and faculty across disciplines have reported that debate-based pedagogy helped improve classroom engagement, critical problem solving, perspective taking, empathy, and advocacy skills. This mixed-method research provides insights not only into debate-based course design and learning improvement strategies but also into how faculty, students, and administrators can partner between institutions to demonstrate a shared commitment to the civic mission of higher education and democratic promise of our nation.

Taxes in the Time of Revolution: An Experimental Test of the Rentier State during Algeria's Hirak

In this paper we examine the rentier thesis that a state's control over oil resources should help it resist calls for democratization. During Algeria's mass mobilization for regime change known as the Hirak in 2019, we implemented an interactive experimental treatment providing specific information about the Algerian government's high subsidies of gasoline and low value-added taxes with regional comparisons. Based on a sample of 5,968 Algerians, we find that when Algerians learn about their country's relatively high level of fuel subsidies and low level of taxes, their assessments of the government's performance improves; however, we do not see similar patterns for respondents' expressed intention to join the protests due to treatment heterogeneity defined by respondent wealth. Wealthier respondents report lower protest intentions upon learning about the scope of the rentier state, whereas poorer respondents report much higher protest intentions upon receiving the treatment. As a result, we find that the rentier state may be capable of improving perceptions of regime performance, yet still permit mass mobilization if there are class differences in the perceived benefits derived from redistribution.

Land and Contestations over Autochthony and Local Citizenship in Agrarian Ghana

This article examines the reimagination of communities in an industrial cassava frontier of Ghana in the wake of a contested land grab supported by state and community institutions. Qualitative and survey data were used to construct the existing social relations in the communities through the lens of earlier processes of agrarian change that have transformed the social base of the communities. It is argued that the expansion of capitalist production systems into agrarian areas results in local citizenship contestations centered on land, and redefinition and reclassification of people and their access to land. The multiple claims and contestations that arose from the land grab and the political reactions from below are highlighted. It is further argued that differentiated dispossession and class differences determine the strategies used by affected people. While some farmers demonstrated agency by holding on to a “little pie” to enjoy greater community social cohesion, others, drawing from their local citizenship status, although contested, fought the land grab.

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.

Steroid use in Pregnancy with Severe COVID-19

Managing women who are pregnant with severe COVID-19 is complex. This paper focuses on the debate surrounding steroid use in this group. Unfortunately, despite international efforts to identify treatments for COVID-19, there is very little research which has focussed specifically on pregnant women. Therefore current guidance is based on consensus and expert opinion, with variation in these guidelines worldwide, and reports that 73% of pregnant women do not receive steroids at all. There is an assumption of a steroid class-effect implicit within the UK guidelines for the mother with COVID-19 which is at odds with established within-class differences for effects on the foetus. This now warrants further discussion given the increasing numbers of pregnant women being admitted to hospital with COVID-19.

Between-Class Earnings Inequality in 30 European Countries

This article studies earnings inequality between social classes across 30 European countries. Class inequality in earnings is found across the board although there are some exceptions. However, the degree of class inequality varies strongly across countries being larger in Western and Southern European countries and smaller in Eastern and Northern European countries. Furthermore, we find that differences in class composition in terms of observed characteristics associated with earnings account for a substantial proportion of these between-class differences. Differences between classes in the returns to education and other characteristics play less of a role. In all these respects there is a sizeable cross-national variation. This points to important differences between countries in how earnings are structured by social class.

Social Mobility in China

This chapter analyses intergenerational class mobility in China as a case study of a quantitative sociological approach to social mobility research in the Global South. Drawing on national representative surveys collected between 2010 and 2015 in China, the analysis focuses on absolute and relative mobility rates for men and women across four birth cohorts. With regard to absolute mobility, we find rising levels of mobility, with upward mobility prevailing over downward mobility. With regard to relative mobility, we find constancy for the older cohorts but a growing rigidity for the youngest cohort of men. The urban–rural divide is increasingly blurred, but class differences are becoming more salient, especially between the professional-managerial salariat and the rest of society in occupational and educational attainment.

Steroid use in Pregnancy with Severe COVID-19

Managing women who are pregnant with severe COVID-19 is complex. This paper focuses on the debate surrounding steroid use in this group. Unfortunately, despite international efforts to identify treatments for COVID-19, there is very little research which has focussed specifically on pregnant women. Therefore current guidance is based on consensus and expert opinion, with variation in these guidelines worldwide, and reports that 73% of pregnant women do not receive steroids at all. There is an assumption of a steroid class-effect implicit within the UK guidelines for the mother with COVID-19 which is at odds with established within-class differences for effects on the foetus. This now warrants further discussion given the increasing numbers of pregnant women being admitted to hospital with COVID-19.

Latent Classes of First Incident Difficulty in Instrumental Activities of Daily Living

Ability to perform instrumental activities of daily living (IADLs) deteriorates during prodromal Alzheimer’s disease (AD), eventually leading to impaired everyday functioning and dementia. Ordering and timing of IADL difficulty onset may identify individuals at greater risk of cognitive impairment, but most studies only consider total number of difficult tasks. Leveraging longitudinal data from the Advanced Cognitive Training in Independent and Vital Elderly (ACTIVE) Study who entered free of any IADL difficulty (N=1266), we hypothesized that a latent class analysis based on timing of first reported IADL task difficulty would reveal class differences in cognitive functioning . Participants were followed until they self-reported at least one IADL difficulty, study completion (10 years), or loss to follow-up. Discrete-time multiple event process survival mixture (MEPSUM) models were used to simultaneously estimate hazards of incident IADL task difficulty across 7 task groups. Two, 3, 4, and 5 latent class models were fit to the data. Both unadjusted and covariate-adjusted models (adjusted for age, sex, race, education, marital status, and general health rating) were fit. Using the 2-class solution as the most parsimonious model, model entropy was 0.855. The model was able to distinguish a class of participants with lower global cognitive factor scores at baseline (Cohen’s D = 0.23, P = 0.04). We conclude that first incident IADL difficulty may be a useful measure in identifying individuals with worse cognitive functioning.

An Automatic Light Stress Grading Architecture Based on Feature Optimization and Convolutional Neural Network

The identification of light stress is crucial for light control in plant factories. Image-based lighting classification of leafy vegetables has exhibited remarkable performance with high convenience and economy. Convolutional Neural Network (CNN) has been widely used for crop image analysis because of its architecture, high accuracy and efficiency. Among them, large intra-class differences and small inter-class differences are important factors affecting crop identification and a critical challenge for fine-grained classification tasks based on CNN. To address this problem, we took the Lettuce (Lactuca sativa L.) widely grown in plant factories as the research object and constructed a leaf image set containing four stress levels. Then a light stress grading model combined with classic pre-trained CNN and Triplet loss function is constructed, which is named Tr-CNN. The model uses the Triplet loss function to constrain the distance of images in the feature space, which can reduce the Euclidean distance of the samples from the same class and increase the heterogeneous Euclidean distance. Multiple sets of experimental results indicate that the model proposed in this paper (Tr-CNN) has obvious advantages in light stress grading dataset and generalized dataset.