Urban ecosystem Services in Latin America: mismatch between global concepts and regional realities?

Urban Ecosystems - Latin America and the Caribbean (LAC) is one of the most urbanized and biologically diverse regions in the world but is often characterized by weak environmental governance and...     

The risks and incentives of disciplinary neoliberal feminism: the case of microfinance

This article develops an analysis of the concept of disciplinary neo-liberal feminism through a focus on gendered poverty alleviation strategies and illustrates the value of this through a discussion of microfinance. By locating this study within an analysis of the expansion of global capital accumulation, the article argues that the liberal frameworks of female empowerment and entrepreneurialism that are central to these programs and to feminism in this form, mask their underlying political, social and economic objectives. In contrast, a Marxist Feminist approach more adequately explains the interplay of class and gender that underpins poverty alleviation strategies. This article argues that in the context of financial crisis and reduced social provision, women living in poverty in the Global South were identified as targets for the expansion of global finance. Their integration into global financial networks via microfinance and other pro-poor strategies has facilitated the expansion of markets for credit while at the same time disciplining market participation through the twin forces of risk and incentive. Disciplinary neo-liberal feminism has underpinned this incorporation of women into global capital accumulation creating profound effects for households and families, with microfinance programs representing important sites of contestation for the politics of class and gender.     

The Times They are A’changing: Multigenerational Family Simulations

Multigenerational households are increasingly affecting both the individual and family as well as community organizations and social policies. Social work and other family studies students can profit from educational modalities that use adult learning applications through a systems life-course perspective, the whole family aging over time. Family simulation software—addressing multigenerational families, such as two or more adult generations living together—builds on a previous paper (Marriage & Family Review, Feb. 2015). Social class, among other demographic and environmental variables, is emphasized. Agent-based family social network simulation of multigenerational families can facilitate experiential learning. An automatically generated life events report, based on both factual data and specific family characteristics, can be used as a classroom case study for role playing and assessing.     

Reclaiming the future: (In)visible dirt borders in Sammy Baloji’s mining photomontages

ABSTRACT

This paper analyses how Baloji’s photographic work Essay on Urban Planning and Mémoire/Kolwezi rely on the dialectic of visibility/invisibility of mining dirt to reveal similarities as well as discrepancies between past and present in capital and labour flows. Conflating archives of humans on display and photographs of abandoned mining sites, Baloji’s figurations of mining dirt unravel how perceptions of dirt were used by the colonial system to impose geographies of exclusion that have become invisible. While they expose how postindependence Congo has failed to appropriate its resources, Baloji’s images of artisanal miners working with mining dirt further epitomise how the disposable objects (often dematerialised via the web) consumed in rich nations involve the disposable lives of workers in the Congo within material dirt borders one can no longer see.     

Non-asymptotic sequential confidence regions with fixed sizes for the multivariate nonlinear parameters of regression

In this paper we consider a sequential design for the estimation of nonlinear parameters of regression with guaranteed accuracy. Non-asymptotic confidence regions with fixed sizes for the least squares estimates are used. The obtained confidence region is valid for finite numbers of data points when the distributions of the observations are unknown.     

Regularization through variable selection and conditional MLE with application to classification in high dimensions

It is often the case that high-dimensional data consist of only a few informative components. Standard statistical modeling and estimation in such a situation is prone to inaccuracies due to overfitting, unless regularization methods are practiced. In the context of classification, we propose a class of regularization methods through shrinkage estimators. The shrinkage is based on variable selection coupled with conditional maximum likelihood. Using Stein's unbiased estimator of the risk, we derive an estimator for the optimal shrinkage method within a certain class. A comparison of the optimal shrinkage methods in a classification context, with the optimal shrinkage method when estimating a mean vector under a squared loss, is given. The latter problem is extensively studied, but it seems that the results of those studies are not completely relevant for classification. We demonstrate and examine our method on simulated data and compare it to feature annealed independence rule and Fisher's rule.     

A ‘nondecimated’ lifting transform

Classical nondecimated wavelet transforms are attractive for many applications. When the data comes from complex or irregular designs, the use of second generation wavelets in nonparametric regression has proved superior to that of classical wavelets. However, the construction of a nondecimated second generation wavelet transform is not obvious. In this paper we propose a new ‘nondecimated’ lifting transform, based on the lifting algorithm which removes one coefficient at a time, and explore its behavior. Our approach also allows for embedding adaptivity in the transform, i.e. wavelet functions can be constructed such that their smoothness adjusts to the local properties of the signal. We address the problem of nonparametric regression and propose an (averaged) estimator obtained by using our nondecimated lifting technique teamed with empirical Bayes shrinkage. Simulations show that our proposed method has higher performance than competing techniques able to work on irregular data. Our construction also opens avenues for generating a ‘best’ representation, which we shall explore.     

Nonparametric control charts based on runs and Wilcoxon-type rank-sum statistics

In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carried out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.