Making a home, finding a job: investigating early housing and employment outcomes for young people leaving care

This paper presents findings from a new study of outcomes for young people leaving care funded by the Department for Education and Skills. It reports findings for a sample of 106 young people in relation to progress made in housing and employment some 12–15 months after leaving care. The generally poor employment outcomes of care leavers are acknowledged, but ingredients that make for success are also highlighted, including the value of settled care and post‐care careers, sound career planning and, significantly, the value of delaying young people’s transitions from care. Early career paths also interconnect with how young people fare in housing, in developing life skills and with other problems in their lives after leaving care. Housing outcomes were more encouraging and predominantly shaped by events after leaving care, and faring well in housing was the factor most closely associated with positive mental well‐being in young people. Some groups that are at risk of faring badly are identified, including young people with mental‐health problems, young people with persistent offending or substance misuse problems and, in some respects, young disabled people. The implications of these findings for leaving care services are considered.     

Rao distance as a measure of influence in the multivariate linear model

Several methods have been suggested to detect influential observations in the linear regression model and a number of them have been extended for the multivariate regression model. In this article we consider the multivariate general linear model, Y = XB + k , which contains the linear regression model and the multivariate regression model as particular cases. Assuming that the random disturbances are normally distributed, the BLUE of v B is also normally distributed. Since the distribution of the BLUE of v B and the distribution of the BLUE of v B in the model with the omission of a set of observations differ, to study the influence that a set of observations has on the BLUE of v B , we propose to measure the distance between both distributions. To do this we use Rao distance.     

Intercultural education and academic achievement: a framework for school-based policies in multilingual schools

The paper reviews quantitative and qualitative research evidence regarding the relationship between intercultural education and academic achievement among students from socially marginalized communities. Intercultural education is conceptualized as including a focus both on generating understanding and respect for diverse cultural traditions and challenging inequitable distribution of resources and educational opportunities across social groups. As such, intercultural education incorporates notions such as critical literacy and culturally responsive education. By definition, socially marginalized communities have experienced social exclusion and discrimination, often over generations. Thus, educators who adopt an intercultural education orientation are also committed to challenging the operation of coercive relations of power within their school environments. The operation of societal power relations that affect marginalized group students’ academic achievement can be conceptualized along a continuum ranging from structural/societal, through structural/educational, to interpersonal. Structural/societal forms of discrimination are largely outside the scope of what educators can influence directly (e.g. housing segregation). However, the research evidence suggests that educators have considerable power to resist and challenge coercive power relations operating at both structural/educational (e.g. curriculum materials) and interpersonal (e.g. classroom interactions) dimensions of the continuum. Thus, the proposed framework represents an explanatory model to account for patterns of school success and failure among marginalized group students and a predictive model to specify educator behaviors that are likely to promote academic achievement.     

Sociological Approaches to Self‐injury

This paper reviews the burgeoning sociological literature on non‐suicidal self‐injury, in which individuals intentionally harm themselves by cutting, burning, scratching, or smashing their body parts. We identify challenges to studying self‐injury, such as conflicting definitions and categorizations. Comparing self‐injury to other behaviors such as suicide, body modification, and self‐mutilation, we assert that non‐suicidal self‐injury deserves its own conceptual category. We explain how a critical sociological approach provides a valuable counterweight to medical and psychological studies of self‐injury. In particular, this paper advances the deviance perspective. Finally, we highlight how technology has allowed self‐injurers to build supportive communities in cyberspace, blurring the line between hidden and public acts. We conclude with suggestions for future directions in the study of self‐injury.     

Cost performance of public infrastructure projects: the nemesis and nirvana of change-orders

The cost performance of a wide range of public sector infrastructure projects completed by a contractor are analysed and discussed. Change-orders after a contract to construct an asset was signed were, on average, found to contribute to a 23.75% increase in project costs. A positive association between an increase in change orders and the contractor’s margin were identified. Taxpayers pay for this additional cost, while those charged with constructing assets are rewarded with an increase in their margins. As the public sector embraces an era of digitisation, there is a need to improve the integration of design and construction activities and engender collaboration to ensure assets can be delivered cost effectively and future-proofed. The research paper provides empirical evidence for the public sector to re-consider the processes that are used to deliver their infrastructure assets so as to reduce the propensity for cost overruns and enable future-proofing to occur.     

Geographic Hotspots of Critical National Infrastructure

Failure of critical national infrastructures can result in major disruptions to society and the economy. Understanding the criticality of individual assets and the geographic areas in which they are located is essential for targeting investments to reduce risks and enhance system resilience. Within this study we provide new insights into the criticality of real‐life critical infrastructure networks by integrating high‐resolution data on infrastructure location, connectivity, interdependence, and usage. We propose a metric of infrastructure criticality in terms of the number of users who may be directly or indirectly disrupted by the failure of physically interdependent infrastructures. Kernel density estimation is used to integrate spatially discrete criticality values associated with individual infrastructure assets, producing a continuous surface from which statistically significant infrastructure criticality hotspots are identified. We develop a comprehensive and unique national‐scale demonstration for England and Wales that utilizes previously unavailable data from the energy, transport, water, waste, and digital communications sectors. The testing of 200,000 failure scenarios identifies that hotspots are typically located around the periphery of urban areas where there are large facilities upon which many users depend or where several critical infrastructures are concentrated in one location.     

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.