Exploring the construction of social class in educational discourse: the rational order of the nation state vs. global uncertainties

This article aims to create intellectual space in which issues of social inequality and education can be analyzed and discussed in relation to the multifaceted and multi-levelled complexities of the modern world. It is divided into three sections. Section One locates the concept of social class in the context of the modern nation state during the period after the Second World War. Focusing particularly on the impact of ‘Fordism’ on social organization and cultural relations, it revisits the articulation of social justice issues in the United Kingdom, and the structures put into place at the time to alleviate educational and social inequalities. Section Two problematizes the traditional concept of social class in relation to economic, technological and sociocultural changes that have taken place around the world since the mid-1980s. In particular, it charts some of the changes to the international labour market and global patterns of consumption, and their collective impact on the re-constitution of class boundaries in ‘developed countries’. This is juxtaposed with some of the major social effects of neo-classical economic policies in recent years on the sociocultural base in developing countries. It discusses some of the ways these inequalities are reflected in education. Section Three explores tensions between the educational ideals of the ‘knowledge economy’ and the discursive range of social inequalities that are emerging within and beyond the nation state. Drawing on key motifs identified throughout, the article concludes with a reassessment of the concept of social class within the global cultural economy. This is discussed in relation to some of the major equity and human rights issues in education today.

Asset allocation, cross-class correlation and the structure of property returns

Practical applications of portfolio optimisation tend to proceed on a “top down” basis where funds are allocated first at asset class level (between, say, bonds, cash, equities and real estate) and then, progressively, at sub-class level (within property to sectors, office, retail, industrial for example). While there are organisational benefits from such an approach, it can potentially lead to sub-optimal allocations when compared to a “global” or “side-by-side” optimisation. This will occur where there are correlations between sub-classes across the asset divide that are masked in aggregation – between, for instance, City offices and the performance of financial services stocks. This paper explores such sub-class linkages using UK monthly stock and property data. Exploratory analysis using clustering procedures and factor analysis suggests that property performance and equity performance are distinctive: there is little persuasive evidence of contemporaneous or lagged sub-class linkages. Formal tests of the equivalence of optimised portfolios using top-down and global approaches failed to demonstrate significant differences, whether or not allocations were constrained. While the results may be a function of measurement of market returns, it is those returns that are used to assess fund performance. Accordingly, the treatment of real estate as a distinct asset class with diversification potential seems justified.

Exact design manifold control of a class of nonlinear singularly perturbed systems

The integral manifold approach captures from a geometric point of view the intrinsic two-time-scale behavior of singularly perturbed systems. An important class of nonlinear singularly perturbed systems considered in this note are fast actuator-type systems. For a class of fast actuator-type systems, which includes many physical systems, an explicit corrected composite control, the sum of a slow control and a fast control, is derived. This corrected control will steer the system exactly to a required design manifold.

Exact design manifold control of a class of nonlinear singularly perturbed systems

The integral manifold approach captures from a geometric point of view the intrinsic two-time-scale behavior of singularly perturbed systems. An important class of nonlinear singularly perturbed systems considered in this note are fast actuator-type systems. For a class of fast actuator-type systems, which includes many physical systems, an explicit corrected composite control, the sum of a slow control and a fast control, is derived. This corrected control will steer the system exactly to a required design manifold.

On the numerical integration of a class of singular perturbation problems

A three-point difference scheme recently proposed in Ref. 1 for the numerical solution of a class of linear, singularly perturbed, two-point boundary-value problems is investigated. The scheme is derived from a first-order approximation to the original problem with a small deviating argument. It is shown here that, in the limit, as the deviating argument tends to zero, the difference scheme converges to a one-sided approximation to the original singularly perturbed equation in conservation form. The limiting scheme is shown to be stable on any uniform grid. Therefore, no advantage arises from using the deviating argument, and the most accurate and efficient results are obtained with the deviation at its zero limit.

Transnational urbanism in Port of Spain: returning middle-class urban elites

Port of Spain, Trinidad offers an ideal context in which to analyze pre-retirement return migration to a Global South urban realm, expanding transnational urban research beyond the conventional focus on Global North metropolitan destinations. In this article, we draw on the transnational narratives of a selected sample of relatively youthful Trinidadians, who have spent many years abroad acquiring education and professional experience, but who have then decided to return in mid-career to the capital region of the island nation of their birth, or of their parent(s). Theoretically, we position these returning professionals as members of a "middling" transnational urban class whose return is at least partly motivated by a desire to "make a difference." Our results contribute to a growing literature that documents the role of transnational middle-class urban elites returning elsewhere in the Carribbean: "middling" transnational urbanism is reshaping key facets of urbanization in the Global South.

Bayesian parameter estimation for latent Markov random fields and social networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.

A neurofuzzy classifier for two class problems

A neurofuzzy classifier identification algorithm is introduced for two class problems. The initial fuzzy base construction is based on fuzzy clustering utilizing a Gaussian mixture model (GMM) and the analysis of covariance (ANOVA) decomposition. The expectation maximization (EM) algorithm is applied to determine the parameters of the fuzzy membership functions. Then neurofuzzy model is identified via the supervised subspace orthogonal least square (OLS) algorithm. Finally a logistic regression model is applied to produce the class probability. The effectiveness of the proposed neurofuzzy classifier has been demonstrated using a real data set.