An Economic Exclusion/Male Peer Support Model Looks at 'Wedfare' and Woman Abuse

In recent years ‘‘welfare reform’’ has become a vehicle for many neo-conservative social commentators to invoke marriage vows as a cure for poverty and the abuse of poor women. Their basic claim is that cohabiting relationships are not only more violent than marriages, but that married couples are happier, healthier, and wealthier than cohabiting ones. A policy then of encouraging cohabitants to marry, they claim, would lead to increased family wealth and decreased family violence. We examine these claims in this article, along with the alternative argument that marriage per se is not a solution to these problems. Alternatively we propose an economic exclusion/male peer support model that explains why many cohabiting men abuse women in intimate relationships. If forcing these couples to marry is not a solution, then structural solutions are necessary, along with progressive policy suggestions that address the antecedents of poverty and abuse.

Thinking Critically About Rural Gender Relations: Toward a Rural Masculinity/Male Peer Support Model of Separation/Divorce Sexual Assault

After decades of neglect, a growing number of scholars have turned their attention to issues of crime and criminal justice in the rural context. Despite this improvement, rural crime research is underdeveloped theoretically, and is little informed by critical criminological perspectives. In this article, we introduce the broad tenets of a multi-level theory that links social and economic change to the reinforcement of rural patriarchy and male peer support, and in turn, how they are linked to separation/divorce sexual assault. We begin by addressing a series of misconceptions about what is rural, rural homogeneity and commonly held presumptions about the relationship of rurality, collective efficacy (and related concepts) and crime. We conclude by recommending more focused research, both qualitative and quantitative, to uncover specific link between the rural transformation and violence against women.

Towards a comprehensive and integrated knowledge-based urban development model : status quo and directions

Incorporating knowledge based urban development (KBUD) strategies in the urban planning and development process is a challenging and complex task due to the fragmented and incoherent nature of the existing KBUD models. This paper scrutinizes and compares these KBUD models with an aim of identifying key and common features that help in developing a new comprehensive and integrated KBUD model. The features and characteristics of the existing KBUD models are determined through a thorough literature review and the analysis reveals that while these models are invaluable and useful in some cases, lack of a comprehensive perspective and absence of full integration of all necessary development domains render them incomplete as a generic model. The proposed KBUD model considers all central elements of urban development and sets an effective platform for planners and developers to achieve more holistic development outcomes. The proposed model, when developed further, has a high potential to support researchers, practitioners and particularly city and state administrations that are aiming to a knowledge-based development.

g4c2c: A model for citizen engagement at arms' length from government

The recognition that Web 2.0 applications and social media sites will strengthen and improve interaction between governments and citizens has resulted in a global push into new e-democracy or Government 2.0 spaces. These typically follow government-to-citizen (g2c) or citizen-to-citizen (c2c) models, but both these approaches are problematic: g2c is often concerned more with service delivery to citizens as clients, or exists to make a show of ‘listening to the public’ rather than to genuinely source citizen ideas for government policy, while c2c often takes place without direct government participation and therefore cannot ensure that the outcomes of citizen deliberations are accepted into the government policy-making process. Building on recent examples of Australian Government 2.0 initiatives, we suggest a new approach based on government support for citizen-to-citizen engagement, or g4c2c, as a workable compromise, and suggest that public service broadcasters should play a key role in facilitating this model of citizen engagement.

Zoledronic acid preserves bone structure and increases survival but does not limit tumour incidence in a prostate cancer bone metastasis model

Background The bisphosphonate, zoledronic acid (ZOL), can inhibit osteoclasts leading to decreased osteoclastogenesis and osteoclast activity in bone. Here, we used a mixed osteolytic/osteoblastic murine model of bone-metastatic prostate cancer, RM1(BM), to determine how inhibiting osteolysis with ZOL affects the ability of these cells to establish metastases in bone, the integrity of the tumour-bearing bones and the survival of the tumour-bearing mice. Methods The model involves intracardiac injection for arterial dissemination of the RM1(BM) cells in C57BL/6 mice. ZOL treatment was given via subcutaneous injections on days 0, 4, 8 and 12, at 20 and 100 µg/kg doses. Bone integrity was assessed by micro-computed tomography and histology with comparison to untreated mice. The osteoclast and osteoblast activity was determined by measuring serum tartrate-resistant acid phosphatase 5b (TRAP 5b) and osteocalcin, respectively. Mice were euthanased according to predetermined criteria and survival was assessed using Kaplan Meier plots. Findings Micro-CT and histological analysis showed that treatment of mice with ZOL from the day of intracardiac injection of RM1(BM) cells inhibited tumour-induced bone lysis, maintained bone volume and reduced the calcification of tumour-induced endochondral osteoid material. ZOL treatment also led to a decreased serum osteocalcin and TRAP 5b levels. Additionally, treated mice showed increased survival compared to vehicle treated controls. However, ZOL treatment did not inhibit the cells ability to metastasise to bone as the number of bone-metastases was similar in both treated and untreated mice. Conclusions ZOL treatment provided significant benefits for maintaining the integrity of tumour-bearing bones and increased the survival of tumour bearing mice, though it did not prevent establishment of bone-metastases in this model. From the mechanistic view, these observations confirm that tumour-induced bone lysis is not a requirement for establishment of these bone tumours.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.