Human rights performance reporting : a study of corporations within Australian financial sector

This paper investigates the human rights performance reporting practices of the top 50 Australian financial service companies listed in the Australian Stock Exchange. All corporate reporting media, including annual reports, Social Responsibility Reports (CSR) and company websites, were reviewed to document their disclosure practices for the current period (2009/2010). In considering a number of international voluntary guidelines on human rights, a content analysis instrument containing 80 specific human rights themes, under 10 general categories, was developed to examine corporate reporting media. The results remain intensely unimpressive. The number of companies that disclosed human rights items is extremely low; the majority of the items were not disclosed by any of the companies under investigation. However, compared to CSR reports and company websites, annual reports were the preferable media used to disclose human rights issues. The result indicates how ineffective the voluntary global guidelines are in ensuring that Australian financial corporations report on their human rights performances.

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.

Variation in the whiteness and brightness of white Australian cashmere associated with farm of origin and fibre attributes

While white cashmere is preferred by processors, its whiteness and brightness is affected by country of origin, amino acid composition, nutrition and cashmere production of goats. This work aimed to quantify the factors which affect the whiteness and brightness of 36 batches of processed Australian white cashmere sourced from nine different farms. The cashmere was tested for tristimulus values brightness (Y) and whiteness, as measured by yellowness (Y-Z). Linear models, relating Y and Y-Z were fitted to farm of origin and other objective measurements. Mean attributes (range) were: mean fibre diameter, 16.9 µm (13.9–20.4 μm); fibre curvature, 45°/mm (31–59°/mm); clean washing yield, 91.3% (79.5–97.3%); Y, 78.7 (74.7–82.2); Y-Z, 11.9 (10.3–13.6). Farm alone accounted for 72% of the variation in Y and 65% of the variation in Y-Z (P < 0.001). Once farm had been taken into account only fibre curvature (P = 0.003) was significant in predicting Y and only clean washing yield (P = 0.047) affected Y-Z. Neither the proportion of the fleece present as guard hair (clean cashmere yield) nor cashmere staple length was a significant determinant of Y or Y-Z. For each 10°/mm increase in fibre curvature Y increased 1.3 units. For each 10% increase in clean washing yield Y-Z declined 0.9 units. Variations in Y and Y-Z among farms were probably related to differences in geographic and climatic conditions and were significantly correlated to cashmere production. The effect of clean washing yield was probably related to a reduction in suint content.

The Australian Broadcasting Corporation’s multiplatform projects : industrial logics of children’s content provision in the digital television era

This paper traces the development of children’s multiplatform commissioning at the Australian Broadcasting Corporation (ABC) in the context of the digitalisation of Australian television. Whilst recent scholarship has focussed on ‘post-broadcast’ or ‘second-shift’ industrial practices, designed to engage view(s)ers with proprietary media brands, less attention has been focussed on children’s and young adults’ television in a public service context. Further, although multiplatform projects in the United States and Britain have been the subject of considerable analysis, less work has attempted to contextualise cultural production in smaller media markets. The paper explores two recent multiplatform projects through textual analysis, empirical research (consisting of interviews with key industry personnel) and an investigation of recent policy documents. The authors argue that the ABC’s mixed diet of children’s programming, featuring an educative or social developmental agenda, is complemented by its appeals to audience ‘participation’, with the Corporation maintaining public service values alongside the need to expand audience reach and the legitimacy of its brand. It finds that the ABC’s historical platform infrastructure, across radio, television and online, have allowed it to move beyond a market failure model to exploit multiplatform synergies competitively in the distribution of Australian children’s content to audiences on-demand.

Sustainable farm families : future directions

The health and wellbeing of all Australians is pivotal for economic and social success of the nation. Current data reveals that the health status of people living in rural and remote populations is poorer than their metropolitan counterparts. However there is a lack of understanding of the specific health statistics of rural farming populations.

The Sustainable Farm Families (SFF) Future Directions program aims to fill this gap by providing ongoing evidence-based information and support to Australia’s agricultural industries, to gain insight into the health, wellbeing and safety of Australia’s rural farming populations.

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.

Variation in the whiteness and brightness of mohair associated with farm, season, and mohair attributes

This work aimed to quantify factors affecting the reflectance attributes of Australian white mohair sourced from five different farms and to evaluate the effect of season and year on mohair grown by goats of known genetic origin in a replicated study. For the season study the mohair was harvested every three months for two years. All goats and their fleeces were weighed. Mid-side samples were tested for fibre diameter attributes, clean washing yield (CWY), staple length (SL) and for tristimulus values X, Y, Z and Y-Z. For the farm study (n = 196), linear models, relating Y, Z and Y-Z were fitted to farm of origin and other objective measurements. For the season and year study (n = 176), data were analysed by ANOVA and then by linear analysis. The variation accounted for by farm alone was: X, 22%; Y, 24%; Z, 12%; Y-Z, 30% (P < 0.001). Once farm had been taken into account, the regression models for X, Y and Z had similar significant terms: mean fibre diameter (MFD), CWY, SL and fibre diameter CV; and correlation coefficients (057–0.65). For Y-Z, in addition to farm only MFD was significant (P = 1.8 × 10−9). While X, Y, Z and Y-Z were significantly associated with clean fleece weight (CFwt), CFwt was not significant in any final model. Season affected mohair Y (P = 2.5 × 10−24), Z (P = 2.3 × 10−20) and Y-Z (P = 6.8 × 10−22). Autumn grown mohair had higher Y and Z, and summer grown mohair had lower Z than mohair grown in other seasons. This resulted in summer grown mohair having the highest Y-Z and winter grown mohair having the lowest Y-Z than mohair grown in other seasons. The differences between years in Y, Z and Y-Z were significant but not large. When Y, Z and Y-Z were modeled with season and other mohair attributes, MFD, CWY, CFwt, incidence of medullated fibre (Med) and sire were also significant terms. This model accounted for 62.1% of the variance. Over the range of Med (0.3–4.2%), Y-Z increased by 11 T units. Increasing CFwt 0.5 kg was associated with a decline in Y-Z of 7.5 T units. The variation in Y, Z and Y-Z associated with sire effects were respectively 2.66, 3.77, and 1.04 T units. In the farm and the season studies increasing MFD was associated with lower Y and Z and higher Y-Z. The extent of the differences in tristimulus values between seasons and years, were unlikely to be of commercial importance. The extent of the differences between farms, and to variations in MFD and Med were large enough to be of commercial importance. Clean mohair colour was artefactually biased by MFD.

Voluntary demand for auditing by farm businesses: an Australian perspective

 This study investigates voluntary demand for auditing by Australian farm businesses, a significant but relatively unexplored segment of the economy. Most farms operate as family partnerships or sole proprietors and we thus focus on incentives to audit arising from internal sources (owner-manager), controlling for traditional incentives arising from external contractual constraints (i.e., debt), organisational characteristics (i.e., size), and agency conflict. We hypothesise that an external audit assists management in enhancing internal control by complementing the process of profit planning and control (budgeting) and that increased family conflict provides an incentive to engage external audit. Of the 457 survey questionnaire respondents, 27% voluntarily engage an external auditor and 66% conduct some formal written planning. Results from logistic regression analyses support the predicted impact of both size and debt on audit, and further support the hypothesised impact of budgeting. The positive association between budgeting and audit confirms the complementary relationship. More importantly, this relationship is not confounded by the combined impact of size and budgeting and debt and budgeting on voluntary audit. In addition, family conflict has no impact on voluntary demand for auditing by farm business.

Prediction intervals for short-term wind farm power generation forecasts

Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.