Social networking and digital gaming media convergence : Classification and its consequences for appropriation

Abstract Within the field of Information Systems, a good proportion of research is concerned with the work organisation and this has, to some extent, restricted the kind of application areas given consideration. Yet, it is clear that information and communication technology deployments beyond the work organisation are acquiring increased importance in our lives. With this in mind, we offer a field study of the appropriation of an online play space known as Habbo Hotel. Habbo Hotel, as a site of media convergence, incorporates social networking and digital gaming functionality. Our research highlights the ethical problems such a dual classification of technology may bring. We focus upon a particular set of activities undertaken within and facilitated by the space – scamming. Scammers dupe members with respect to their ‘Furni’, virtual objects that have online and offline economic value. Through our analysis we show that sometimes, online activities are bracketed off from those defined as offline and that this can be related to how the technology is classified by members – as a social networking site and/or a digital game. In turn, this may affect members’ beliefs about rights and wrongs. We conclude that given increasing media convergence, the way forward is to continue the project of educating people regarding the difficulties of determining rights and wrongs, and how rights and wrongs may be acted out with respect to new technologies of play online and offline.

Emotional climate and behavioral management during sleep time in early childhood education settings

The majority of children cease napping between 3 and 5 years of age yet, internationally, the allocation of a sleep time during the day for children of this age remains a practice in many early childhood education (ECE) settings. These dual circumstances present a disjuncture between children's sleep needs and center practices, that may cause conflict for staff, increase stress for children and escalate negative emotional climate in the room. Testing this hypothesis requires observation of both the emotional climate and behavioral management used in ECE rooms that extends into the sleep time. This study was the first to apply the Classroom Assessment and Scoring System (CLASS) Pre-K (Pianta, La Paro, & Hamre, 2008) to observe the emotional climate and behavioral management during sleep time. Pilot results indicated that the CLASS Pre-K functioned reliably to measure emotional climate and behavioral management in sleep time. However, new sleep-specific examples of the dimensions used were developed, to help orient fieldworkers to the CLASS Pre-K rating system in the sleep time context. The CLASS was then used to assess emotional climate and behavior management between the non-sleep and sleep time sessions, in 113 ECE rooms in Queensland, Australia. In these rooms 2.114 children were observed. Of these children, 71% did not sleep at any point during the allotted sleep times. There was a significant drop in emotional climate and behavioral management between the non-sleep and sleep-time sessions. Furthermore, the duration of mandated sleep time (a period of time where no activities are provided to non-sleeping children) accounted for significant independent variance in the observed emotional climate during sleep-time. The CLASS Pre-K presents a valuable tool to assess the emotional climate and behavior management during sleep-time and draws attention to the need for further studies of sleep time in ECE settings.

Modelling attitudes to climate change : an order effect and a test between alternatives

Quantum-like models can be fruitfully used to model attitude change in a social context. Next steps require data, and higher dimensional models. Here, we discuss an exploratory study that demonstrates an order effect when three question sets about Climate Beliefs, Political Affiliation and Attitudes Towards Science are presented in different orders within a larger study of n=533 subjects. A quantum-like model seems possible, and we propose a new experiment which could be used to test between three possible models for this scenario.

Can social resilience inform SA/SIA for adaptive planning for climate change in vulnerable regions?

Social resilience concepts are gaining momentum in environmental planning through an emerging understanding of the socio-ecological nature of biophysical systems. There is a disconnect, however, between these concepts and the sociological and psychological literature related to social resilience. Further still, both schools of thought are not well connected to the concepts of social assessment (SA) and social impact assessment (SIA) that are the more standard tools supporting planning and decision-making. This raises questions as to how emerging social resilience concepts can translate into improved SA/SIA practices to inform regional-scale adaptation. Through a review of the literature, this paper suggests that more cross-disciplinary integration is needed if social resilience concepts are to have a genuine impact in helping vulnerable regions tackle climate change.

Detection of rain in acoustic recordings of the environment

Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.

The influence of fleet safety climate on occupational driver behaviours

The concepts of traffic safety culture and climate hold considerable impact on road safety outcomes. Data sourced from four Australian organisations revealed a five factor structure that was consistent with previous research, which were: management commitment; work demands; relationships; appropriateness of rules; and communication. Correlation and regression analyses were conducted to identify which aspects of fleet safety climate were related to driver behaviours. The findings suggest that organisations may be able to reduce the likelihood of employees engaging in unsafe driving behaviours as a result of fatigue or distractions through increasing aspects of fleet safety climate, including: management commitment; level of trust; safety communication; appropriateness of work demands; and appropriateness of safety policies and procedures. To assist practitioners in enhancing fleet safety climate and managing occupational road risks, recommendations are made based on these findings, such as fostering a supportive environment of mutual responsibility.

Locality-sensitive hashing for protein classification

Determination of sequence similarity is a central issue in computational biology, a problem addressed primarily through BLAST, an alignment based heuristic which has underpinned much of the analysis and annotation of the genomic era. Despite their success, alignment-based approaches scale poorly with increasing data set size, and are not robust under structural sequence rearrangements. Successive waves of innovation in sequencing technologies – so-called Next Generation Sequencing (NGS) approaches – have led to an explosion in data availability, challenging existing methods and motivating novel approaches to sequence representation and similarity scoring, including adaptation of existing methods from other domains such as information retrieval. In this work, we investigate locality-sensitive hashing of sequences through binary document signatures, applying the method to a bacterial protein classification task. Here, the goal is to predict the gene family to which a given query protein belongs. Experiments carried out on a pair of small but biologically realistic datasets (the full protein repertoires of families of Chlamydia and Staphylococcus aureus genomes respectively) show that a measure of similarity obtained by locality sensitive hashing gives highly accurate results while offering a number of avenues which will lead to substantial performance improvements over BLAST..

Resilience of water supply systems in meeting the challenges posed by climate change and population growth

This research project provides a scientifically robust approach for assessing the resilience of water supply systems, which are critical infrastructure, to impacts of climate change and population growth. An approach for the identification of trigger points that allows timely and appropriate management actions to be taken to avoid catastrophic system failure is an important outcome of this project. In the current absence of a formal method to evaluate the resilience of a water supply system, the approach developed in this study was based on the characterisation of resilience of a water supply system to a range of surrogate measures. Accordingly, a set of indicators are proposed to evaluate system behaviour and logistic regression analysis was used to assess system behaviour under predicted rainfall, storage and demand conditions.