[R] statistics question about a statement in julian faraway's "extending the linear model with R" text

[markleeds at verizon.net]

In Julian Faraway's text on pgs 117-119, he gives a very nice, pretty 
simple description of how a glm can be thought of as linear model
with non constant variance. I just didn't understand one of his 
statements  on the top of 118. To quote :

"We can use a similar idea to fit a GLM. Roughly speaking, we want to 
regress g(y) on X with weights inversely proportional
to var(g(y). However, g(y) might not make sense in some cases - for 
example in the binomial GLM. So we linearize g(y)
as follows: Let eta = g(mu) and mu = E(Y). Now do a one step expanation 
, blah, blah, blah.

Could someone explain ( briefly is fine ) what he means by g(y) might 
not make sense in some cases - for example in the binomial
GLM ?

Thanks.