Language and disability in Japan

Language relating to disability in the public arena has been a sensitive issue in Japan as elsewhere. Since the 1970s and 80s, major media organisations have replaced words considered derogatory with more acceptable equivalents; laws, statutes and other legal documents have likewise been revised. This article examines how the language used to portray people with disabilities has changed, how the changes came about and how they were received. The debate has largely been played out in four public spaces, which to some extent intersect and overlap: the media (both print and visual), the laws, literature and, increasingly now, the Internet. I argue that while the laws were rewritten primarily as the result of external international trends, such as the International Year of Disabled Persons, disability groups achieved media compliance mainly by exploiting the keen desire of Japanese media organisations to avoid public embarrassment resulting from vocal protests over infractions.

The effects of normalisation on the ability of business end-users to detect data anomalies: An experimental evaluation

With the proliferation of relational database programs for PC's and other platforms, many business end-users are creating, maintaining, and querying their own databases. More importantly, business end-users use the output of these queries as the basis for operational, tactical, and strategic decisions. Inaccurate data reduce the expected quality of these decisions. Implementing various input validation controls, including higher levels of normalisation, can reduce the number of data anomalies entering the databases. Even in well-maintained databases, however, data anomalies will still accumulate. To improve the quality of data, databases can be queried periodically to locate and correct anomalies. This paper reports the results of two experiments that investigated the effects of different data structures on business end-users' abilities to detect data anomalies in a relational database. The results demonstrate that both unnormalised and higher levels of normalisation lower the effectiveness and efficiency of queries relative to the first normal form. First normal form databases appear to provide the most effective and efficient data structure for business end-users formulating queries to detect data anomalies.