In this special edition on virtual-world goods and trade, we are pleased to present articles from a global cohort of contributors covering a wide range of issues. Some of our writers, such Edward Castronova, Julian Dibbell or KZero’s Nic Mitham will be well known to you as distinguished leaders in the field, but it is equally our pleasure to introduce exciting new voices. Here you will find pieces written by academics, practitioners, journalists, a documentary filmmaker and perhaps the youngest contributor to JVWR yet, Eli Kosminksy, who attends high school in upstate New York. We would also point out that this issue extends its format to include Anthony Gilmore’s pictorial story, Julian Dibbell’s audio interview, and Lori Landay’s machinima. In real life, most contributors live in the US, the UK and Europe, and we, the editors, are based in Australia and France. We express warm thanks to the team at the University of Texas, especially to Jeremiah Spence, our editor–in-chief for his guidance throughout this process. We begin with our own thought piece, which is designed to contextualise the deeper contents herein by way of plotting the virtual goods path and placing some historical sign posts along the way.Mandy and Serge

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An approach for supporting large vocabulary in speech recognition is to use broad phonetic classes to reduce the search to a subset of the dictionary. In this paper, we investigate the problem of defining an optimal classification for a given speech decoder, so that these broad phonetic classes are recognized as accurately as possible from the speech si gnal . More precisely, given Hidden Markov Models of phonemes, we define a similarity measure of the phonetic machines, and use a standard classification algorithm to find the optimal classification. Three measures are proposed, and compared with manual classifications.

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