Modeling Demand For Unionization With Nontraditional Data Analysis Methods

Upon reviewing the extant literature on determinants of unionism, it becomes clear that many areas that have had a plethora of research attention do not converge upon singularly directional findings. This study explores a potential cause of such an apparent anomaly: nonlinearity of data. An exploratory examination of correlation coefficients among typical union determinant variables seems to show different patterns of relationships at different levels of union demand. Thus, a break from traditional linear data analysis techniques is explored in the interest of explaining more variance with typical, theoretically derived variables by using neural network analysis. Results of analyses on industry level data reveal that using neural network analysis to model union demand explained over four times as much variance as multiple regression analysis.     

The Impact of Learning and Labor Attrition on Worker Flexibility in Dual Resource Constrained Job Shops

This study investigates the impact of worker learning, worker flexibility, and labor attrition on the system performance of a dual resource constrained (DRC) job-shop. The effects of learning and labor attrition have not been previously addressed in DRC literature. Results from the study, consistent with previous literature, show that the greatest benefits are achieved when inter-departmental worker flexibility is incrementally introduced into the system. In addition, the learning environment, which depends on the initial processing time of jobs and the learning rates of workers, is shown to impact the acquisition of flexibility. The study also shows that the impact of labor attrition on system performance under certain shop conditions may be significant.     

Capturing temporal dynamics of fear behaviors on a moment‐to‐moment basis

Identifying patterns of fearful behaviors early and accurately is essential to identify children who may be at increased risk for psychopathology. Previous work focused on the total amount of fear by using composites across time. However, considering the temporal dynamics of fear expression might offer novel insights into the identification of children at risk. One hundred and twenty‐five toddlers participated in high‐ and low‐fear tasks. Data were modeled using a novel two‐step approach. First, a hidden Markov model estimated latent fear states and transitions across states over time. Results revealed children's behavior was best represented by six behavioral states. Next, these states were analyzed using sequence clustering to identify groups of children with similar dynamic trajectories through the states. A four‐cluster solution found groups of children varied in fear response and regulation process: “external regulators” (using the caregiver as a regulation tool), “low reactive” (low reaction to stimulus), “fearful explorers” (managing their own internal state with minimal assistance from the caregiver), and “high fear” (fearful/at‐caregiver state regardless of task). The combination of analytic tools enabled fine‐grained examination of the processes of fearful temperament. These insights may help prevention programs target behaviors that perpetuate anxious behavior in the moment.     

Sensitivity Analysis in Linear Programming Models with Common Inputs

The importance of sensitivity analysis information in linear programming has been stressed in the management science literature for some time. Indeed, Gal [3] has devoted an entire text to just this issue. Linear programs with common inputs (cost coefficients or right-hand-side values) present a problem in that classical sensitivity analysis does not allow for the simultaneous changes required to determine the sensitivity of these models to common inputs. We first survey the approaches previously developed for simultaneous-change sensitivity analysis and cast them in the framework of the special common input case. These general techniques are compared to a simple aggregate variable technique that has not received attention in the literature.     

Protecting and Benchmarking Migrants’ Rights: An Analysis of the Global Compact for Safe,Orderly and Regular Migration

The Global Compact for Safe, Orderly, and Regular Migration (GCM) was to be “guided by human rights law and standards” in recognition of the rights of international migrants, who are currently protected by an overlapping patchwork of treaties and international law. The GCM contains many laudable commitments that, if implemented, will ensure that states more consistently respect, protect, and fulfil the rights of all migrants and also that states incorporate data on migration into a more cohesive governance regime that does more to promote cooperation on the issue of international migration. However, many concerns remain. Using a legal analysis and cross‐national policy data, we find that the GCM neither fully articulates existing law nor makes use of international consensus to expand the rights of migrants. In its first section, this article provides a concise analysis of the GCM's compliance with a set of core principles of existing international human rights law regarding migrants. In the second section, we apply a novel instrument to create an objective, cross‐national accounting of the laws protecting migrants’ rights in various national legal frameworks. Focusing on a sample of five diverse destination and sending countries, the results suggest we are close to an international consensus on the protection of a core set of migrants’ rights. This analysis should help prioritize the work necessary to implement the GCM.     

The Popular Kids Don't Matter: Centrality and Influence on Adolescent Behavior

This study examines the widely held belief that socially central individuals are disproportionately influential in their networks. Using data from the National Longitudinal Study of Adolescent Health, it models the association between four behavioral outcomes and two distinct specifications of the behaviors' relationship to network prominence. This study finds little evidence that sociometrically central individuals are more influential than randomly chosen peers from the same network when predicting drinking, smoking, and sports participation. Students resemble their peers in systematic ways, but it is unlikely that this is because central students serve as a reference for the group or because students adjust their actions based on the social rewards that they observe given their position in a social network.     

Expediting youth's entry into employment whilst overlooking precariousness: Flexi‐employability and disciplinary activation in Hong Kong

With an emphasis placed on supply‐side interventions such as skills training and incentives enhancement, active labor market polices (ALMPs) are strongly promoted by international organizations and widely adopted across different welfare regimes to boost employment rates. This article first presents the under‐examined relationship between ALMPs and employment precariousness, which has posed a challenge to the neoliberal notion of employability and activation. Youth‐focused employment policies tend to speed up employment entry whilst downplaying the risk of precariousness and the importance of job quality, and thus further reinforcing the belief that engaging in precarious employment is tolerable if not inevitable. The article then examines the case of Hong Kong, which illustrates that its relatively low rate of youth unemployment may conceal the unfavorable employment conditions confronted by youth. It is argued that the service‐led employment policies and short‐term vocational training define the employability of young workers in terms of labor flexibilities. The coined phase of “flexi‐employability” is characterized by promoting youth's readiness for, and adaptability to, the volatilities and changing demands of the labor market. Arguably, the disciplinary approach to youth activation would only strengthen the work‐first principle by enforcing young people to take up jobs available and leave welfare as soon as possible, but without thoroughly addressing the risks and insecurities generated by the labor market in undermining their well‐being.     

Methods for spatial and temporal land use and land cover assessment for urban ecosystems and application in the greater Baltimore-Chesapeake region

Understanding contemporary urban landscapes requires multiple sets of spatially and temporally compatible data that can integrate historical land use patterns and disturbances to land cover. This paper presents three principal methods: (1) core analysis; (2) historic mapping; and (3) gradient analysis, to link spatial and temporal data for urban ecosystems and applies their use in the Baltimore-Chesapeake region. Paleoecological evidence derived from the geochronology of sediment cores provides data on long-term as well as recent changes in vegetative land cover. This information, combined with contemporary vegetation maps, provides a baseline for conducting trend analyses to evaluate urbanization of the landscape. A 200-year historical land use database created from historical maps, census data, and remotely sensed data provides a spatial framework for investigating human impacts on the region. A geographic information system (GIS) integrates core analyses with historic data on land use change to yield a comprehensive land use and land cover framework and rates of change. These data resources establish the regional foundation for investigating the ecological components of an urban ecosystem. Urban-rural gradient analyses and patch analyses are proposed as the most appropriate methods for studying the urban ecosystem as they link ecological and social patterns and processes for varying degrees of urbanization.     

Development of the Emergency Department Senior Abuse Identification (ED Senior AID) tool

Emergency departments (EDs) are an important health care setting for the identification of elder abuse (EA). Our objective was to develop an ED-based tool to identify EA. The initial tool included a brief cognitive assessment, questions to detect multiple domains of EA, and a physical examination. Refinement of the tool was based on input from clinical experts and nurse and patient feedback. The revised tool, which included 15 questions about EA, was then tested in an academic ED. We calculated the inter-rater reliability, sensitivity, and specificity of individual EA questions. Among ED patients age≥65 (N = 259), 17 (7%) screened positive for suspicion of EA. We identified a combination of six questions that cover the included domains of EA, demonstrated good or excellent inter-rater reliability, and had a sensitivity and specificity of 94% (95% confidence interval (CI) 71–100%) and 90% (95% CI 85–93%), respectively. These results inform a proposed screening tool for multisite validation testing.     

Tracing disorder across theories of autism,empathy, and mental health care

Abstract

Categories of mental disorders are generally understood through a biomedical paradigm of clinical research, diagnosis, and intervention. Here, diagnoses operate as professional tools, facilitating care organization and information exchange across diverse social contexts. In this article, I focus on how the diagnosis of autism-spectrum disorder operates in this manner. Autism conceived as a biomedical disorder is then contrasted with proposals by the neurodiversity movement, who understand core qualities of autism as alternative expressions of otherwise normal processes of brain development. Finally, I supplement these conversations with insights from Gerald Edelman’s theory of neural plasticity and Felix Guattari’s paradigm of ethico-aesthetic care. Understood together, these allow mental disorders and community care generally to be reconceived in terms of networks of expressive, embodied, and dynamically embedded rhythms that transcend individual persons. This serves, additionally, to illustrate a concept of empathy that traverses neurological, psychological, and sociocultural domains.