Youth as an artefact of expertise : problematizing the practice of youth studies in an age of uncertainty

At the start of the 21st century the Liberal democracies, including Australia, are characterized by profound social, economic and cultural transformations. Community, policy and academic discourse is marked by widespread adult anxieties about today’s young people. Representations of youth in the institutional domain of ‘youth studies’ can be conceived as artefacts of the activities of diverse forms of expertise. This paper will focus on the institutionalized processes of abstraction which construct these truths, and the roles played by these processes of abstraction in the restless problematization of ‘youth’ as the object of countless competing and complementary governmental programmes. There has been, in recent years, increased debate about how to do youth studies and how to represent youth. The paper will argue that any rethinking or reassessment of the modes of representing youth ought to take some account of the institutional and abstract nature of these processes of representation, and of the implication of these processes in the regulation of populations of young people; populations which are rendered knowable in all their diversity only through these processes of representation.

The effects of perceived business uncertainty, external consultants and risk management on organisational outcomes

Purpose – The purpose of this paper is to investigate the relations between perceived business uncertainty (PBU), use of external risk management (RM) consultants, formalisation of RM, magnitude of RM methods and perceived organisational outcomes.
Design/methodology/approach – This paper is based on a questionnaire survey of members of the Chartered Institute of Management Accountants in the UK. Using AMOS 17.0, the paper tests the strength of the direct and indirect effects among the variables and explores the fit of the overall path model.
Findings – The results indicate significant and positive associations exist between the extent of PBU and the level ofRMformalisation, as well as between the level ofRMformalisation and the magnitude of RMmethods adopted. The use of externalRMconsultants is also found to have a significant and positive impact on the magnitude of RM methods adopted. Finally, both the extent of RM formalisation and the magnitude of RM methods adopted are seen to be significantly associated with overall improvement in organisational outcomes.
Research limitations/implications – The study uses perceptual measures of the level of business uncertainty, usage of RM and organisational outcomes. Further, the respondents are members of a management accounting professional body and the views of other managers, such as risk managers, who are also important to the governance process are not incorporated.
Originality/value – This study provides empirical evidence on the impact ofRMdesign and usage on improvements in organisational outcomes. It contributes to the RM literature where empirical research is needed in order to be comparable with the traditional management control system literature.

Critical uncertainty factors for efficient risk allocation in privately financed public infrastructure projects in Australia

Risk allocation in privately financed public infrastructure projects, commonly referred to as public-private partnership (PPP) projects, is a challenging job due to the nature of incomplete contracting. Choosing a risk allocation strategy could be viewed as the process of deciding the proportion of risk management attributable to the public and private partners based on a series of characteristics of the risk management service transaction in question. These characteristics can be related to the various uncertainty factors. In this study, uncertainty factors have been grouped into Institutional, Social and industrial, Economic, and Project-specific categories and examined in order to achieve efficient risk allocation and minimize risk management-related costs in a long-term view. Critical uncertainty factors for the allocation of three major risks have been identified through an industry-wide survey in Australia. These identified critical uncertainty factors are expected to help decision-makers from both public and private sectors choose efficient allocation strategies for major risks. Future research directions are also set out.

Critical uncertainty factors for efficient allocation of demand risk in privately financed public infrastructure projects in Australia

Risk allocation in privately financed public infrastructure projects, which are mainly referred as public-private partnership (PPP) projects, is a challenging job due to the nature of incomplete contracting. Among the various risks that may eventually materialise, demand risk is one of the major challenges that PPPs face. Choosing a risk allocation strategy could be viewed as the process of deciding the proportion of risk management responsibility between public and private partners based on a series of characteristics of risk management service transaction in question. These characteristics are more or less related to the various uncertainty factors. In this study, various uncertainty factors have been examined in order to achieve efficient allocation of demand risk and minimise risk management-related costs in a long-term view. Critical uncertainty factors have been identified through an industry-wide survey in Australia. Future research directions are also set out.

Uncertainty quantification for wind farm power generation

Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.

Minimizing bounded uncertainty impact on scheduling with earliest start and due-date constraints via interval computation

Bounded uncertainty is a major challenge to real life scheduling as it increases the risk and cost depending on the objective function. Bounded uncertainty provides limited information about its nature. It provides only the upper and the lower bounds without information in between, in contrast to probability distributions and fuzzymembership functions. Bratley algorithm is usually used for scheduling with the constraints of earliest start and due-date. It is formulated as . The proposed research uses interval computation to minimize the impact of bounded uncertainty of processing times on Bratley’s algorithm. It minimizes the uncertainty of the estimate of the objective function. The proposed concept is to do the calculations on the interval values and approximate the end result instead of approximating each interval then doing numerical calculations. This methodology gives a more certain estimate of the objective function.

Wind farm power uncertainty quantification using a mean-variance estimation method

This paper proposes an innovative optimized parametric method for construction of prediction intervals (PIs) for uncertainty quantification. The mean-variance estimation (MVE) method employs two separate neural network (NN) models to estimate the mean and variance of targets. A new training method is developed in this study that adjusts parameters of NN models through minimization of a PI-based cost functions. A simulated annealing method is applied for minimization of the nonlinear non-differentiable cost function. The performance of the proposed method for PI construction is examined using monthly data sets taken from a wind farm in Australia. PIs for the wind farm power generation are constructed with five confidence levels between 50% and 90%. Demonstrated results indicate that valid PIs constructed using the optimized MVE method have a quality much better than the traditional MVE-based PIs.

Understanding the sources of uncertainty to reduce the risks of undesirable outcomes in large-scale freshwater ecosystem restoration projects: an example from the Murray–Darling Basin, Australia

There are a growing number of large-scale freshwater ecological restoration projects worldwide. Assessments of the benefits and costs of restoration often exclude an analysis of uncertainty in the modelled outcomes. To address this shortcoming we explicitly model the uncertainties associated with measures of ecosystem health in the estuary of the Murray– Darling Basin, Australia and how those measures may change with the implementation of a Basin-wide Plan to recover water to improve ecosystem health. Specifically, we compare two metrics – one simple and one more complex – to manage end-of-system flow requirements for one ecosystem asset in the Basin, the internationally important Coorong saline wetlands. Our risk assessment confirms that the ecological conditions in the Coorong are likely to improve with implementation of the Basin Plan; however, there are risks of a Type III error (where the correct answer is found for the wrong question) associated with using the simple metric for adaptive management. 

PANIC in the presence of uncertainty about the deterministic trend

Most macroeconomic and financial panel variables are trending. However, because of the well-known power problem in the presence of incidental trends, many researchers gamble that their unit root test regressions can be ran without such trends, thereby running the risk of obtaining spurious results. This article takes one of the most general and popular panel unit root tests, known as PANIC, and shows how it can be modified to account for the uncertainty regarding the deterministic trend.

Oil price uncertainty and sovereign risk: evidence from Asian economies

In this paper, we test whether oil price uncertainty predicts credit default swap (CDS) returns for eight Asian countries. We use the Westerlund and Narayan, 2011 and Westerlund and Narayan, 2012 predictability test that accounts for any persistence in and endogeneity of the predictor variable. The estimator also accounts for any heteroskedasticity in the regression model. In-sample evidence reveals that oil price uncertainty predicts CDS returns for three Asian countries, whereas out-of-sample evidence suggests that oil price uncertainty predicts CDS returns for six countries.

Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming

Uncertainty of data affects decision making process as it increases the risk and the costs of the decision. One of the challenges in minimizing the impact of the bounded uncertainty on any scheduling algorithm is the lack of information, as only the upper bound and the lower bound are provided without any known probability or membership function. On the contrary, probabilistic uncertainty can use probability distributions and fuzzy uncertainty can use the membership function. McNaughton's algorithm is used to find the optimum schedule that minimizes the makespan taking into consideration the preemption of tasks. The challenge here is the bounded inaccuracy of the input parameters for the algorithm, namely known as bounded uncertain data. This research uses interval programming to minimise the impact of bounded uncertainty of input parameters on McNaughton’s algorithm, it minimises the uncertainty of the cost function estimate and increase its optimality. This research is based on the hypothesis that doing the calculations on interval values then approximate the end result will produce more accurate results than approximating each interval input then doing numerical calculations.

Australian Mothers' notions of risk and uncertainty in relation to their pre-teen children

In this article we examine the ways discourses of risk manifested and played out within and across two groups of Australian mothers living in two large urban centres in Australia: the first comprised of mothers who had a pre-teen child diagnosed with an eating disorder (n = 13); the second of mothers who had a pre-teen child without the symptoms or diagnosis of an eating disorder (n = 13). In 2011 and 2012, we conducted in-depth interviews with the mothers in their homes on their ideas about health and their relationships with their children. An analysis of the data collected from these interviews indicated that having a pre-teen child diagnosed with an eating disorder had a decisive impact on how the mothers constituted and responded to risk. For mothers, who had a pre-teen child with an eating disorder, risk was intensified by bio-medical discourses. The particular intensifications of risk limited the ways in which mothers could act and often threatened to undermine their abilities as competent carers. By contrast, the mothers who did not have a pre-teen child with an eating disorder spoke about risk less directly, and with less sense of immediacy. Where these mothers acknowledged risk discourses particularly in regard to health, they were in a stronger position to negotiate them. Our analysis indicates that the ways in which mothers responded to risk is contingent on circumstances and contexts. Mothers’ responses to risk were related to the calculability of the risk and their perceived capacity to manage it.