Performativity, youth and injurious speech

Young people may become disengaged from schooling in the middle years for a multitude of reasons. We consider the story of one young woman from the state of New South Wales, in Australia, who left school early, and consider some of the factors that contributed to her decision to remove herself from compulsory education. This young woman encountered injurious speech relating to her race, gender, sexuality, size and ability. In undertaking this analysis, we draw on Foucaultian theorizing of the subject and on the related Butlerian notion of performativity. Performative acts that occur within and around schools have the power to injure, to alienate and to potentially exclude students from access to schooling. This article details how performative acts may operate as mechanisms of exclusion, obfuscating the social conventions and institutional structures that invest them with power. Our analysis of how performative acts function in school settings concludes with some suggestions of how teachers and students might think differently about the production of their own and others' existence.

Towards an interactive architecture for web-based databases

World Wide Web has brought us a lot of challenges, such as infinite contents, resource diversity, and maintenance and update of contents. Web-based database (WBDB) is one of the answers to these challenges. Currently the most commonly used WBDB architecture is three-tier architecture, which is still somehow lack of flexibility to adapt to frequently changed user requirements. In this paper, we propose a hybrid interactive architecture for WBDB based on the reactive system concepts. In this architecture, we use sensors to catch users frequently changed requirements and use a decision making manager agent to process them and generate SQL commands dynamically. Hence the efficiency and flexibility are gained from this architecture, and the performance of WBDB is enhanced accordingly.

The use of injury surveillance databases to identify emerging injury hazards

Among the many valuable uses of injury surveillance is the potential to alert health authorities and societies in general to emerging injury trends, facilitating earlier development of prevention measures. Other than road safety, to date, few attempts to forecast injury data have been made, although forecasts have been made of other public health issues. This may in part be due to the complex pattern of variance displayed by injury data. The profile of many injury types displays seasonality and diurnal variance, as well as stochastic variance. The authors undertook development of a simple model to forecast injury into the near term. In recognition of the large numbers of possible predictions, the variable nature of injury profiles and the diversity of dependent variables, it became apparent that manual forecasting was impractical. Therefore, it was decided to evaluate a commercially available forecasting software package for prediction accuracy against actual data for a set of predictions. Injury data for a 4-year period (1996 to 1999) were extracted from the Victorian Emergency Minimum Dataset and were used to develop forecasts for the year 2000, for which data was also held. The forecasts for 2000 were compared to the actual data for 2000 by independent t-tests, and the standard errors of the predictions were modelled by stepwise hierarchical multiple regression using the independent variables of the standard deviation, seasonality, mean monthly frequency and slope of the base data (R = 0.93, R² = 0.86, F_{(3, 27)} = 55.2, p < 0.0001). Significant contributions to the model included the SD (β = 1.60, p < 0.001), mean monthly frequency (β =  - 0.72, p < 0.002), and the seasonality of the data (β = 0.16, p < 0.02). It was concluded that injury data could be reliably forecast and that commercial software was adequate for the task. Variance in the data was found to be the most important determinant of prediction accuracy. Importantly, automated forecasting may provide a vehicle for identifying emerging trends.

Pattern discovery in probabilistic databases

Modeling probabilistic data is one of important issues in databases due to the fact that data is often uncertainty in real-world applications. So, it is necessary to identify potentially useful patterns in probabilistic databases. Because probabilistic data in 1NF relations is redundant, previous mining techniques don’t work well on probabilistic databases. For this reason, this paper proposes a new model for mining probabilistic databases. A partition is thus developed for preprocessing probabilistic data in a probabilistic databases. We evaluated the proposed technique, and the experimental results demonstrate that our approach is effective and efficient.

Mining small databases by collecting knowledge

Current data mining techniques may not be helpful for mining some companies/organizations such as nuclear power plants and earthquake bureaus, which have only small databases. Apparently, these companies/organizations also expect to apply data mining techniques to extract useful patterns in their databases so as to make their decisions. However, data in these databases such as the accident database of a nuclear power plant and the earthquake database in an earthquake bureau, may not be large enough to form any patterns. To meet the applications, we present a new mining model in this paper, which is based on the collecting knowledge from such as Web, journals, and newspapers.

Representing the mind: adult talk to speech-delayed preschoolers

There is evidence from a range of studies that adult input influences a young child's language development Of interest is how adult input contributes to emotional and cognitive understandings. Children with special needs in mainstream schools are expected to develop social skills which entails the understanding of a situation from the perspective of other participants. The question is whether children with a speech delay hear adult language that helps them develop a theory of mind. The study of the acquisition of a theory of mind has focused on children who have been asked to carry out tasks demonstrating their understanding of what another person might be thinking. Tager-Flusberg et al. (2001) have found that children who perform better on theory of mind tasks are children who talk about thoughts and feelings. The present study looks at mental state language input provided to children that might help them learn to talk about thoughts and feelings. Activities involving children and their mothers, and activities in a preschool program were studied for cognitive and emotional content in the adult input. The input provided to normally developing children would be more supportive of the development of their talk about thoughts and feelings.

Analyzing inconsistency toward enhancing integration of biological molecular databases

The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.

Detecting inconsistency in biological molecular databases using ontologies

The rapid growth of life science databases demands the fusion of knowledge from heterogeneous databases to answer complex biological questions. The discrepancies in nomenclature, various schemas and incompatible formats of biological databases, however, result in a significant lack of interoperability among databases. Therefore, data preparation is a key prerequisite for biological database mining. Integrating diverse biological molecular databases is an essential action to cope with the heterogeneity of biological databases and guarantee efficient data mining. However, the inconsistency in biological databases is a key issue for data integration. This paper proposes a framework to detect the inconsistency in biological databases using ontologies. A numeric estimate is provided to measure the inconsistency and identify those biological databases that are appropriate for further mining applications. This aids in enhancing the quality of databases and guaranteeing accurate and efficient mining of biological databases.

Developmental language disorders and adolescent risk: a public-health advocacy role for speech pathologists?

Within the multi-disciplinary team concerned with child and adolescent development, speech pathologists are uniquely positioned to understand the nature and overall developmental significance of language acquisition in childhood and adolescence. Other disciplines contribute valuable insights about psychosocial development during the childhood and adolescent years. The field of developmental psychology, for example provides a large and convincing body of evidence about the role of academic success as a protective factor against a range of psychosocial harms, in particular substance misuse, truancy, early school leaving, and juvenile offending. In this paper, we argue that juvenile offending embodies the notion of "adolescent risk", but in Australia in particular, has been under-investigated with respect to possible associations with developmental language disorders and subsequent academic failure. We present findings pertaining to a sample of 30 male juvenile offenders completing community based orders. Performance on a range of oral language processing and production skills was poorer than that of a demographically similar comparison group. Our results confirm the need to conceptualize language within a broader risk and protective framework. We therefore emphasize the public health importance of early language competence, by virtue of the psychosocial protection it confers on young people with respect to the development of prosocial skills, transition to literacy and overall academic achievement. We argue that speech pathologists are best positioned to advocate at a policy level about the broader public health importance of oral language competence.