Justifications in young children’s language in an early childhood classroom

This paper examines the interactional phenomenon of justification as it is produced in young children’s language. A justification provides a reason for one’s position and can be produced in children’s language at an early age. There are various pragmatic reasons for justifications. For example, justifications may be drawn upon by members to compensate for the disruption of the existing social order or to explain something that is possibly questionable. Justifications are also drawn upon to extend or close disputes. This study uses the analytical techniques of conversation analysis and membership categorisation to analyse video-recorded and transcribed interactions of young children (aged 4-6 years) in a preparatory classroom in a primary school in Australia. The focus is an episode that occurred within the block play area of the classroom that involved a dispute of ownership relating to a small, wooden plank. In analysing this dispute, justifications were frequent occurrences and the young participants drew upon justificatory devices in their everyday arguments. As the turns surrounding the justificatory language were examined, a pattern emerged: in each excerpt observed, a justification arose in response to a challenge. This pattern provided the basis for developing a model that helped to discern where, why and what type of justifications occurred in the interaction. To depict this interactional phenomenon, the model of ‘if x, then y’ was used, ‘x’ referring to the challenge or prompt, and ‘y’ referring to the justificatory response. Justifications related to the concepts of ownership and were used as devices by those engaged in disputes to support their positions and provide reasons for their actions. The children drew upon these child-constructed rules as resources to use in disputes with their peers, in order to construct and maintain the social order of the block area in the classroom.

Ethyl-eicosapentaenoic acid in first-episode psychosis : a 1H-MRS study

Ethyl-eicosapentaenoic acid (E-EPA) is an omega-3 fatty acid that has been used in a range of neuropsychiatric conditions with some benefits. However, its mechanism of action is unknown. Here, we investigate its effects on in vivo brain metabolism in first-episode psychosis (FEP). Proton magnetic resonance spectroscopy at 3 T was performed in the temporal lobes of 24 FEP patients before and after 12 weeks of treatment in the context of a larger double-blind, placebo-controlled E-EPA augmentation study. Treatment group effects for glutathione (F1,12=6.1, p=0.03), and a hemisphere-by-group interaction for glutamine/glutamate (F1,20=4.4, p=0.049) were found. Glutathione increased bilaterally and glutamate/glutamine increased in the left hemisphere following E-EPA administration. Improvement in negative symptoms correlated with metabolic brain changes, particularly glutathione (r=-0.57). These results suggest that E-EPA augmentation alters glutathione availability and modulates the glutamine/glutamate cycle in early psychosis, with some of the metabolic brain changes being correlated with negative symptom improvement. Larger confirmatory studies of these postulated metabolic brain effects of E-EPA are warranted.

A 1H-MRS investigation of the medial temporal lobe in antipsychotic-naïve and early-treated first episode psychosis

Schizophrenia is associated with significant brain abnormalities, including changes in brain metabolites as measured by proton magnetic resonance spectroscopy (MRS). What remains unclear is the extent to which these changes are a consequence of the emergence of psychotic disorders or the result of treatment with antipsychotic medication. We assessed 34 patients with first episode psychosis (15 antipsychotic naïve) and 19 age- and gender-matched controls using short-echo MRS in the medial temporal lobe bilaterally. Overall, there were no differences in any metabolite, regardless of treatment status. However, when the analysis was limited to patients with a diagnosis of schizophrenia, schizophreniform or schizoaffective disorder, significant elevations of creatine/phosphocreatine (Cr/PCr) and myo-inositol (mI) were found in the treated group. These data indicate a relative absence of temporal lobe metabolic abnormalities in first episode psychosis, but suggest that some treatment-related changes in mI might be apparent in patients with schizophrenia-spectrum diagnoses. Seemingly illness-related Cr/PCr elevations were also specific to the diagnosis of schizophrenia-spectrum disorder and seem worthy of future study.

A Reliable Basis for Approximate Association Rules

For most of the work done in developing association rule mining, the primary focus has been on the efficiency of the approach and to a lesser extent the quality of the derived rules has been emphasized. Often for a dataset, a huge number of rules can be derived, but many of them can be redundant to other rules and thus are useless in practice. The extremely large number of rules makes it difficult for the end users to comprehend and therefore effectively use the discovered rules and thus significantly reduces the effectiveness of rule mining algorithms. If the extracted knowledge can’t be effectively used in solving real world problems, the effort of extracting the knowledge is worth little. This is a serious problem but not yet solved satisfactorily. In this paper, we propose a concise representation called Reliable Approximate basis for representing non-redundant approximate association rules. We prove that the redundancy elimination based on the proposed basis does not reduce the belief to the extracted rules. We also prove that all approximate association rules can be deduced from the Reliable Approximate basis. Therefore the basis is a lossless representation of approximate association rules.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach,which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Concise representations for association rules in multi-level datasets

Association rule mining has made many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we firstly propose a definition for redundancy; then we propose a concise representation called Reliable basis for representing non-redundant association rules for both exact rules and approximate rules. An important contribution of this paper is that we propose to use the certainty factor as the criteria to measure the strength of the discovered association rules. With the criteria, we can determine the boundary between redundancy and non-redundancy to ensure eliminating as many redundant rules as possible without reducing the inference capacity of and the belief to the remaining extracted non-redundant rules. We prove that the redundancy elimination based on the proposed Reliable basis does not reduce the belief to the extracted rules. We also prove that all association rules can be deduced from the Reliable basis. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules.