A window on relationships: reflecting critically on a current restorative justice theory

Inherent in the task of theorising is a responsibility for ongoing critical reflection of the ideas presented (Steele, 2010). To that end, this article responds to the invitation extended by McCold and Wachtel to examine the conceptual theory of restorative justice they first presented in 2003 and which continues to be promoted globally. One particular aspect of their theory, the Social Discipline Window, is examined. Drawing on a qualitative, critical case study conducted in schools in Ontario, Canada, the article illustrates: (a) how unexamined theory can be problematic and promote practice that counters the principles of restorative justice; and (b) how people's lives can be impacted by power dynamics inherent in the theory presented (Woolford, 2009). In response, a revised Relationship Window is presented along with examples of how it can affect practice that is more consistently aligned with the philosophical foundations of restorative justice.

Glia-augmented artificial neural networks: foundations and applications

Information processing in the human brain has always been considered as a source of inspiration in Artificial Intelligence; in particular, it has led researchers to develop different tools such as artificial neural networks. Recent findings in Neurophysiology provide evidence that not only neurons but also isolated and networks of astrocytes are responsible for processing information in the human brain. Artificial neural net- works (ANNs) model neuron-neuron communications. Artificial neuron-glia networks (ANGN), in addition to neuron-neuron communications, model neuron-astrocyte con- nections. In continuation of the research on ANGNs, first we propose, and evaluate a model of adaptive neuro fuzzy inference systems augmented with artificial astrocytes. Then, we propose a model of ANGNs that captures the communications of astrocytes in the brain; in this model, a network of artificial astrocytes are implemented on top of a typical neural network. The results of the implementation of both networks show that on certain combinations of parameter values specifying astrocytes and their con- nections, the new networks outperform typical neural networks. This research opens a range of possibilities for future work on designing more powerful architectures of artificial neural networks that are based on more realistic models of the human brain.