What does 's' denote in describing a General Linear Model (GLM)?

The GLM terminology is described [http://www.fil.ion.ucl.ac.uk/~mgray/ here] in relation to using SPM.

Examples of GLMs include linear regressions and analysis of variance and are of form.

Y = XB + error
or, in words,
Response = Prediction + residual

s is, therefore, the residual standard deviation which, for example, corresponds to the square root of the mean square error term in an analysis of variance.

Note: The use of Mean Square Error is equivalent to the 'Deviance' Scale Parameter Method using the default linear model (for a continuous response) under the 'Estimation' tab in the Generalized Linear Model (under 'analyze' in SPSS). The default setting Scale Parameter method in the 'Generalized Linear Model' is 'Log-likelihood' which yields a statistic which has a critical value following a chi-square distribution. This statistic is not usually used directly for regression/anova models with continuous responses but is incorporated as a denominator in a F ratio. It is, however, used directly for categorical responses ie we tend to quote chi-square values rather than F values to assess the influence of predictor variables on group responses.