Engaging with theories of dialogue and voice: Using Bakhtin as a framework to understand teaching and learning Indigenous Australian women's performance

In this paper I explore the Indigenous Australian women's performance classroom (hereafter ANTH2120) as a dialectic and discursive space where the location of possibility is opened for female Indigenous performers to enter into a dialogue from and between both non-Indigenous and Indigenous voices. The work of Bakhtin on dialogue serves as a useful standpoint for understanding the multiple speaking positions and texts in the ANTH2120 context. Bakhtin emphasizes performance, history, actuality and the openness of dialogue to provide an important framework for analysing multiple speaking positions and ways of making meaning through dialogue between shifting and differing subjectivities. I begin by briefly critiquing Bakhtin's "dialogic imagination" and consider the application and usefulness of concepts such as dialogism, heteroglossia and the utterance to understanding the ANTH2120 classroom as a polyphonic and discursive space. I then turn to an analysis of dialogue in the ANTH2120 classroom and primarily situate my gaze on an examination of the interactions that took place between the voices of myself as family/teacher/student and senior Yanyuwa women from the r e m o t e N o r t h e r n T e r r i t o r y A b o r i g i n a l c o m m u n i t y o f B o r r o l o o l a as family/performers/teachers. The 2000 and 2001 Yanyuwa women's performance workshops will be used as examples of the way power is constantly shifting in this dialogue to allow particular voices to speak with authority, and for others to remain silent as roles and relationships between myself and the Yanyuwa women change. Conclusions will be drawn regarding how my subject positions and white race privilege affect who speaks, who listens and on whose terms, and further, the efficacy of this pedagogical platform for opening up the location of possibility for Indigenous Australian women to play a powerful part in the construction of knowledges about women's performance traditions.

The long-term effects of cooperative learning on children's behaviour and interactions

This study investigates the long-term effects of training in small-group and interpersonal behaviours on children's behaviours and interactions as they worked in small groups two years after they were initially trained. Forty-eight third grade children, who had been trained two years previously in cooperative group behaviours, were assigned to the Trained condition and 44 third grade children who had not previously been trained were assigned to the Untrained condition. The children in the trained and untrained groups were reconstituted from the pool of students who had participated previously in either trained or untrained group activities. The results showed that there was a long-term training effect with the children in the Trained groups demonstrating more cooperative behaviour and providing more explanations in response to requests for help than their untrained peers.

The residual effects of cooperative-learning experiences: A two-year follow-up

The author investigated how training in small-group and interpersonal behaviors affected children's behavior and interactions as they worked in small groups 2 years later. The authors assigned 52 fifth graders, who had been trained 2 years previously in cooperative group behaviors, to the trained condition and 36 fifth graders, who had not previously been trained, to the untrained condition. Both were reconstituted from the pool of students who had participated previously in group activities. The results showed a residual training effect, with the children in the trained groups being more cooperative and helpful than their untrained peers.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

MR image-based measurement of rates of change in volumes of brain structures. Part 1: Method and validation

A detailed analysis procedure is described for evaluating rates of volumetric change in brain structures based on structural magnetic resonance (MR) images. In this procedure, a series of image processing tools have been employed to address the problems encountered in measuring rates of change based on structural MR images. These tools include an algorithm for intensity non-uniforniity correction, a robust algorithm for three-dimensional image registration with sub-voxel precision and an algorithm for brain tissue segmentation. However, a unique feature in the procedure is the use of a fractional volume model that has been developed to provide a quantitative measure for the partial volume effect. With this model, the fractional constituent tissue volumes are evaluated for voxels at the tissue boundary that manifest partial volume effect, thus allowing tissue boundaries be defined at a sub-voxel level and in an automated fashion. Validation studies are presented on key algorithms including segmentation and registration. An overall assessment of the method is provided through the evaluation of the rates of brain atrophy in a group of normal elderly subjects for which the rate of brain atrophy due to normal aging is predictably small. An application of the method is given in Part 11 where the rates of brain atrophy in various brain regions are studied in relation to normal aging and Alzheimer's disease. (C) 2002 Elsevier Science Inc. All rights reserved.

Input-driven Language Learning

Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.

A subset-oriented algorithm for minimizing the number of steps required for synthesis of cyclic-peptide libraries

Libraries of cyclic peptides are being synthesized using combinatorial chemistry for high throughput screening in the drug discovery process. This paper describes the min_syn_steps.cpp program (available at http://www.imb.uq.edu.au/groups/smythe/tran), which after inputting a list of cyclic peptides to be synthesized, removes cyclic redundant sequences and calculates synthetic strategies which minimize the synthetic steps as well as the reagent requirements. The synthetic steps and reagent requirements could be minimized by finding common subsets within the sequences for block synthesis. Since a brute-force approach to search for optimum synthetic strategies is impractically large, a subset-orientated approach is utilized here to limit the size of the search. (C) 2002 Elsevier Science Ltd. All rights reserved.