Deterred by debt? Young people,schools and the escalating cost of UK higher education

Popular commentaries lament that reforms to the financing of Higher Education (HE) in the UK will operate as a significant deterrent to HE participation amongst students from less advantaged backgrounds. This view of debt as a deterrent is powerful and exists not only at a societal level, but also, as we show, is present in schools. Our data reveal, however, that these ‘debt commentaries’ play out very differently across schools according to the nature of their catchment and the sorts of views staff hold about pupils in relation to their fear of debt. Furthermore, students’ views on debt largely contradict these popular ‘debt-as-deterrent’ narratives and instead are often characterised by acceptance, ambivalence and at times positive orientations towards the prospect of debt. These findings vividly illuminate both the regional and institutional specificity of staff and student decision making in relation to ‘debt’, hence they have substantial implications for HE funding policy specifically and debates about widening participation in HE more generally, where ensuring greater equity in the scope of choices young people have when it comes to choosing a university is a pressing concern.     

Difference-based Methods for Truncating the Singular Value Decomposition

Given a noisy time series (or signal), one may wish to remove the noise from the observed series. Assuming that the noise-free series lies in some low-dimensional subspace of rank r, a common approach is to embed the noisy time series into a Hankel trajectory matrix. The singular value decomposition is then used to deconstruct the Hankel matrix into a sum of rank-one components. We wish to demonstrate that there may be some potential in using difference-based methods of the observed series in order to provide guidance regarding the separation of the noise from the signal, and to estimate the rank of the low-dimensional subspace in which the true signal is assumed to lie.     

Childbearing Across Partnerships in Australia,the United States,Norway, and Sweden

This article compares mothers’ experience of having children with more than one partner in two liberal welfare regimes (the United States and Australia) and two social democratic regimes (Sweden and Norway). We use survey-based union and birth histories in Australia and the United States and data from national population registers in Norway and Sweden to estimate the likelihood of experiencing childbearing across partnerships at any point in the childbearing career. We find that births with new partners constitute a substantial proportion of all births in each country we study. Despite quite different arrangements for social welfare, the determinants of childbearing across partnerships are very similar. Women who had their first birth at a very young age or who are less well-educated are most likely to have children with different partners. The educational gradient in childbearing across partnerships is also consistently negative across countries, particularly in contrast to educational gradients in childbearing with the same partner. The risk of childbearing across partnerships increased dramatically in all countries from the 1980s to the 2000s, and educational differences also increased, again, in both liberal and social democratic welfare regimes.     

New Data,Strategies, and Insights for Listeria monocytogenes Dose‐Response Models: Summary of an Interagency Workshop, 2011

Listeria monocytogenes is a leading cause of hospitalization, fetal loss, and death due to foodborne illnesses in the United States. A quantitative assessment of the relative risk of listeriosis associated with the consumption of 23 selected categories of ready‐to‐eat foods, published by the U.S. Department of Health and Human Services and the U.S. Department of Agriculture in 2003, has been instrumental in identifying the food products and practices that pose the greatest listeriosis risk and has guided the evaluation of potential intervention strategies. Dose‐response models, which quantify the relationship between an exposure dose and the probability of adverse health outcomes, were essential components of the risk assessment. However, because of data gaps and limitations in the available data and modeling approaches, considerable uncertainty existed. Since publication of the risk assessment, new data have become available for modeling L. monocytogenes dose‐response. At the same time, recent advances in the understanding of L. monocytogenes pathophysiology and strain diversity have warranted a critical reevaluation of the published dose‐response models. To discuss strategies for modeling L. monocytogenes dose‐response, the Interagency Risk Assessment Consortium (IRAC) and the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) held a scientific workshop in 2011 (details available at http://foodrisk.org/irac/events/ ). The main findings of the workshop and the most current and relevant data identified during the workshop are summarized and presented in the context of L. monocytogenes dose‐response. This article also discusses new insights on dose‐response modeling for L. monocytogenes and research opportunities to meet future needs.     

Mini-batch learning of exponential family finite mixture models

Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectation–maximization (EM) algorithm framework, we demonstrate how mini-batch (MB) algorithms may be constructed, and propose a scheme for the stochastic stabilization of the constructed mini-batch algorithms. Theoretical results regarding the convergence of the mini-batch EM algorithms are presented. We then demonstrate how the mini-batch framework may be applied to conduct maximum likelihood (ML) estimation of mixtures of exponential family distributions, with emphasis on ML estimation for mixtures of normal distributions. Via a simulation study, we demonstrate that the mini-batch algorithm for mixtures of normal distributions can outperform the standard EM algorithm. Further evidence of the performance of the mini-batch framework is provided via an application to the famous MNIST data set.     

Collaboration and Internationalization of SMEs: Insights and Recommendations from a Systematic Review

This paper performs a systematic literature review of the undeniably diverse – and somewhat fragmented – current state of research on the collaborations and internationalization of small and medium‐sized enterprises (SMEs). We analyze key works and synthesize them into a framework that conceptually maps key antecedents, mediators, and moderators that influence the internationalization of SMEs. In addition, we highlight limitations of the literature, most notably in terms of theoretical fragmentation; extant theories are deployed and illustrated but rarely extended in a manner that significantly informs subsequent work. At an applied (but related) level, we argue the need for supplementary work that explores the distinct stages of internationalization – and the scope and scale of this process – rather than assuming closure around particular events. With this, we highlight the need for more rigorous and empirically informed explorations of contextual effects that take account of the consequences of developments in the global economic ecosystem.     

Finie mixures of canonical fundamenal skew $$$$-disribuions

This paper introduces a finite mixture of canonical fundamental skew \(t\) (CFUST) distributions for a model-based approach to clustering where the clusters are asymmetric and possibly long-tailed (in: Lee and McLachlan, arXiv:1401.8182 [statME], 2014b). The family of CFUST distributions includes the restricted multivariate skew \(t\) and unrestricted multivariate skew \(t\) distributions as special cases. In recent years, a few versions of the multivariate skew \(t\) (MST) mixture model have been put forward, together with various EM-type algorithms for parameter estimation. These formulations adopted either a restricted or unrestricted characterization for their MST densities. In this paper, we examine a natural generalization of these developments, employing the CFUST distribution as the parametric family for the component distributions, and point out that the restricted and unrestricted characterizations can be unified under this general formulation. We show that an exact implementation of the EM algorithm can be achieved for the CFUST distribution and mixtures of this distribution, and present some new analytical results for a conditional expectation involved in the E-step.