Language, respect and the power of protest: Linguistic stereotyping and people with disabilities in Japan

Discriminatory language became an important social issue in the west in the late twentieth century, when debates on political correctness and minority rights focused largely on the issue of respect in language. Japan is often criticized for having made only token attempts to address this issue. This paper investigates how one marginalized group—people with disabilities—has dealt with discriminatory and disrespectful language. The debate has been played out in four public spaces: the media, the law, literature, and the Internet. The paper discusses the kind of language, which has generated protest, the empowering strategies of direct action employed to combat its use, and the response of the media, the bureaucracy, and the literati. Government policy has not kept pace with social change in this area; where it exists at all, it is often contradictory and far from clear. I argue that while the laws were rewritten primarily as a result of external international trends, disability support groups achieved domestic media compliance by exploiting the keen desire of media organizations to avoid public embarrassment. In the absence of language policy formulated at the government level, the media effectively instituted a policy of self-censorship through strict guidelines on language use, thereby becoming its own best watchdog. Disability support groups have recently enlisted the Internet as an agent of further empowerment in the ongoing discussion of the issue.

Work at hand: an exploration of gesture in the context of work and everyday life to inform the design of gestural input devices

This research explores gestures used in the context of activities in the workplace and in everyday life in order to understand requirements and devise concepts for the design of gestural information appliances. A collaborative method of video interaction analysis devised to suit design explorations, the Video Card Game, was used to capture and analyse how gesture is used in the context of six different domains: the dentist's office; PDA and mobile phone use; the experimental biologist's laboratory; a city ferry service; a video cassette player repair shop; and a factory flowmeter assembly station. Findings are presented in the form of gestural themes, derived from the tradition of qualitative analysis but bearing some similarity to Alexandrian patterns. Implications for the design of gestural devices are discussed.

Linguistic determinism and theory of mind in deaf children

DeVilliers and DeVilliers (2000, 2005) propose that deaf and hearing children acquire a theory of mind (or the understanding that human behaviour is the product of psychological states like true and false beliefs) as a consequence of their linguistic mastery of a rule of syntax. Specifically, they argue that the syntactic rule for sentential complementation with verbs of speech (e.g., “say”) precedes syntactic mastery of complementation for cognition (e.g., “think”) and both of these developmentally precede and promote conceptual mastery of a theory of mind (ToM), as indexed via success on standard false belief tests. The present study examined this proposition in groups of primary-school-aged deaf children and hearing preschoolers who took false belief tests and a modified memory for complements test that included control questions. Guttman scaling techniques indicated no support either for the prediction that syntactic skill precedes ToM understanding or for the earlier emergence of complementation for “say” than for “think”. Methodological issues and implications for deaf children's ToM development are discussed.

On the relationship between Bayesian error bars and the input data density

We investigate the dependence of Bayesian error bars on the distribution of data in input space. For generalized linear regression models we derive an upper bound on the error bars which shows that, in the neighbourhood of the data points, the error bars are substantially reduced from their prior values. For regions of high data density we also show that the contribution to the output variance due to the uncertainty in the weights can exhibit an approximate inverse proportionality to the probability density. Empirical results support these conclusions.

Regression with input-dependent noise: A Gaussian process treatment

Gaussian processes provide natural non-parametric prior distributions over regression functions. In this paper we consider regression problems where there is noise on the output, and the variance of the noise depends on the inputs. If we assume that the noise is a smooth function of the inputs, then it is natural to model the noise variance using a second Gaussian process, in addition to the Gaussian process governing the noise-free output value. We show that prior uncertainty about the parameters controlling both processes can be handled and that the posterior distribution of the noise rate can be sampled from using Markov chain Monte Carlo methods. Our results on a synthetic data set give a posterior noise variance that well-approximates the true variance.

Regression with input-dependent noise: A Bayesian treatment

In most treatments of the regression problem it is assumed that the distribution of target data can be described by a deterministic function of the inputs, together with additive Gaussian noise having constant variance. The use of maximum likelihood to train such models then corresponds to the minimization of a sum-of-squares error function. In many applications a more realistic model would allow the noise variance itself to depend on the input variables. However, the use of maximum likelihood to train such models would give highly biased results. In this paper we show how a Bayesian treatment can allow for an input-dependent variance while overcoming the bias of maximum likelihood.