Towards understanding stakeholder salience transition and relational approach to ‘better’ corporate social responsibility: a case for a proposed model in practice

Management and business literature affirm the role played by stakeholders in corporate social responsibility (CSR) practices as crucial, but what constitutes a true business–society partnership remains relatively unexplored. This paper aims to improve scholarly and management understanding beyond the usual managers’ perceptions on salience attributes, to include how stakeholders can acquire missing attributes to inform a meaningful partnership. In doing this, a model is proposed which conceptualises CSR practices and outcomes within the frameworks of stakeholder salience via empowerment, sustainable corporate social performances and partnership quality. A holistic discussion leads to generation of propositions on stakeholder salience management, corporate social performance, corporate–community partnership systems and CSR practices, which have both academic and management implications.

Denitrification losses from an intensively managed sub-tropical pasture: Impact of soil moisture on the partitioning of N2 and N2O emissions

Intensively managed pastures in subtropical Australia under dairy production are nitrogen (N) loaded agro-ecosystems, with an increased pool of N available for denitrification. The magnitude of denitrification losses and N2:N2O partitioning in these agro-ecosystems is largely unknown, representing a major uncertainty when estimating total N loss and replacement. This study investigated the influence of different soil moisture contents on N2 and N2O emissions from a subtropical dairy pasture in Queensland, Australia. Intact soil cores were incubated over 15 days at 80% and 100% water-filled pore space (WFPS), after the application of 15N labelled nitrate, equivalent to 50 kg N ha−1. This setup enabled the direct quantification of N2 and N2O emissions following fertilisation using the 15N gas flux method. The main product of denitrification in both treatments was N2. N2 emissions exceeded N2O emissions by a factor of 8 ± 1 at 80% WFPS and a factor of 17 ± 2 at 100% WFPS. The total amount of N-N2 lost over the incubation period was 21.27 kg ± 2.10 N2-N ha−1 at 80% WFPS and 25.26 kg ± 2.79 kg ha−1 at 100% WFPS respectively. N2 emissions remained high at 100% WFPS, while related N2O emissions decreased. At 80% WFPS, N2 emissions increased constantly over time while N2O fluxes declined. Consequently, N2/(N2 + N2O) product ratios increased over the incubation period in both treatments. N2/(N2 + N2O) product ratios responded significantly to soil moisture, confirming WFPS as a key driver of denitrification. The substantial amount of fertiliser lost as N2 reveals the agronomic significance of denitrification as a major pathway of N loss for sub-tropical pastures at high WFPS and may explain the low fertiliser N use efficiency observed for these agro-ecosystems.

Fraudsters change tactics as a crackdown cuts some losses due to online scams

The amount of financial loss from online fraud suffered by people in Western Australia has almost halved, dropping from A$16.8 million in 2014 to A$9.8 million for 2015, according to a statement this January from the state’s Attorney General and Minister for Commerce, Michael Mischin. In addition, the minister noted that losses from relationship and dating fraud have fallen by 55%, to A$4.9 million lost last year. These are both impressive claims, and at face value, there is truth to the statistics. Both assertions are based on data received by WA’s Scamnet, which is the public interface between consumer protection and citizens. While it is good to see a reduction in the number of losses overall, particularly to relationship and dating fraud, it is highly unlikely that the statistics tell the full story.

Professional learning places and spaces: The staffroom as a site of beginning teacher induction and transition

This paper argues that the staffroom is an important professional learning space where beginning teachers interact to understand who they are and the nature of their professional work. The authors highlight the theoretical importance of space and place in the construction and negotiation of beginning teacher subjectivities. To illustrate the staffroom as a particular place where important professional learning could occur the authors use two narratives based on the lived experiences of two beginning teachers, one in a primary context, the other secondary. The authors conclude by calling for greater research attention to the significance of the staffroom and its interaction with teacher subjectivities. At the level of practice we also call for the teaching profession to recognise staffrooms as important sites of professional learning and places that should support induction and mentoring of beginning teachers. Such recognition could enhance the retention, satisfaction, and effectiveness of new and experienced teachers alike.

Generalized self-assembly of scalable two-dimensional transition metal oxide nanosheets

Two-dimensional (2D) transition metal oxide systems present exotic electronic properties and high specific surface areas, and also demonstrate promising applications ranging from electronics to energy storage. Yet, in contrast to other types of nanostructures, the question as to whether we could assemble 2D nanomaterials with an atomic thickness from molecules in a general way, which may give them some interesting properties such as those of graphene, still remains unresolved. Herein, we report a generalized and fundamental approach to molecular self-assembly synthesis of ultrathin 2D nanosheets of transition metal oxides by rationally employing lamellar reverse micelles. It is worth emphasizing that the synthesized crystallized ultrathin transition metal oxide nanosheets possess confined thickness, high specific surface area and chemically reactive facets, so that they could have promising applications in nanostructured electronics, photonics, sensors, and energy conversion and storage devices.

The mesenchymal-to-epithelial reverting transition is enriched in metastatic castration resistant prostate cancer and correlates with poor survival

Despite recent recognition that the epithelial-mesenchymal transition (EMT) program acts in a dynamic manner (termed Epithelial to Mesenchymal Plasticity or EMP) during carcinoma metastasis, it has largely been ignored in the discovery and development of EMT-targeted therapies. In part, this has stemmed from a lack of preclinical models that can mimic the full dynamic nature of EMP and the perception that the EMT-reverting transition [or mesenchymal-epithelial reverting transition; (MErT)] is a mere antithesis of EMT. The objective of this study was to develop the first PCa model capable of recapitulating the dynamic nature of EMP.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.