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Editorial

Published online by Cambridge University Press:  21 November 2013

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Copyright © Cambridge University Press 2013 

As citizens of the Information Age, we have access to computer resources that allow us to look at language in ways that were unthinkable even a decade ago. Indeed, current developments in Internet technologies are now enabling us to look quite literally at the language of a text or corpus by transforming it into a visual image that can be explored on-screen.

For readers interested in trying out this new technology, an excellent place to start is WordWanderer (http://wordwanderer.org). WordWanderer provides three different kinds of visualisation, each with its own distinctive features. The first of these, Cloud View, presents an alphabetical list of the most frequently-occurring words in the text(s) under analysis, with relative frequencies indicated by different font sizes: the bigger the font, the more frequently-occurring the word is. Hover your mouse pointer over any of the words on the screen, and the collocations of that word are instantly highlighted in colour, with darker tones indicating stronger associations, and lighter tones indicating weaker relationships.

In the second kind of visualisation, Context View, a selected word appears in the middle of the screen with collocates of that word placed around it. While font size once again indicates relative frequency, Context View also uses on-screen positioning to add further information to the picture. Collocates positioned to the left are more likely to occur before the node word than after it in a phrase or clause, while collocates positioned to the right are more likely to occur afterwards.

Finally, the user can generate a Comparison View of any two words simply by clicking on one word and dragging the mouse cursor to another. This causes the two selected words to move to the far left and far right of the screen respectively, with collocations of one or both of these words appearing in between, relative physical location indicating relative strength of attraction.

Text visualisation is an exciting technology because it can make abstract concepts such as collocation and colligation accessible to non-specialist audiences. It also allows researchers to identify characteristic topics and themes in databases that are too large to study through traditional ‘close reading’ methods. So it has the potential to bring English language studies into dialogue with the dynamic new cross-disciplinary field of digital humanities. We hope to see more studies making productive use of text visualisation tools in forthcoming issues of English Today.

This issue sees the start of a venture in which English Today links up with a team from the University of Leiden in The Netherlands, led by Professor Ingrid Tieken-Boon van Ostade, which is conducting large-scale research into prescriptivism in English. Here, we publish an article in which the project is introduced, and in subsequent issues it is expected that various members of the research team will raise particular issues of interest, the expectation being that readers will wish to respond and so to contribute to the findings of the Leiden researchers. We also have a strong African flavour to this issue, with three articles from the continent: Takam addresses the perennially knotty matter of prepositional use; Rupp and Akhidenor tackle very different matters of code-mixing in West African and Southern African contexts respectively. Then, changing the geographical focus, Nair and Rosli Talif's article explores the transmission of gender messages in children's English literature in Malaysia. Finally, Lampert analyses the performance of three celebrities to take a fresh look at quotatives. Reviews cover a wide spectrum of interests: Herring reviews the BBC's electronic output from its large variation-corpus Voices project; and Bacchini and Ferguson evaluate books on very different topics of grammar and variation.

We are most grateful to the following for peer-reviewing selected articles for English Today in 2013: Sean Bowerman, Andrew Moody, Kay McCormick, Jon Orman, Mike Pearce, Pam Peters, Robert Penhallurick, Mario Saraceni, Edgar Schneider and Katie Wales.