SI
SI
discoversearch

We've detected that you're using an ad content blocking browser plug-in or feature. Ads provide a critical source of revenue to the continued operation of Silicon Investor.  We ask that you disable ad blocking while on Silicon Investor in the best interests of our community.  If you are not using an ad blocker but are still receiving this message, make sure your browser's tracking protection is set to the 'standard' level.
Pastimes : Laughter is the Best Medicine - Tell us a joke

 Public ReplyPrvt ReplyMark as Last ReadFilePrevious 10Next 10PreviousNext  
To: John Messbauer who wrote (11276)8/29/1999 9:11:00 PM
From: c.horn  Read Replies (1) of 62558
 
A language instructor was explaining to her
class that French nouns, unlike their English counterparts, are grammatically
designated as masculine or feminine. Things like "chalk" or "pencil," she described, would have a gender association.

For example:
House is feminine---"la" maison. In English, of course, words are of neutral gender.

Puzzled, one student raised his hand and asked, "What gender is a computer?"

The teacher wasn't certain which it was, and
so divided the class into two groups and asked them to decide if a computer should be masculine or feminine. One group was comprised of the women in the class,and the other of men. Both groups were asked to give four reasons
for their recommendation

The men decided that computers should definitely be referred to in the feminine gender (la) because:

1. No one but their creator understands their
internal logic.
2. The native language they use to communicate
with other computers is incomprehensible to everyone else.
3. Even the smallest mistakes are stored in
long-term memory for later retrieval.
4. As soon as you make a commitment to one,
you find yourself spending half your paycheck on accessories for it.

The group of women, however, concluded that
computers should be referred to in the masculine (le) gender because:

1. In order to get their attention, you have
to turn them on.
2. They have a lot of data but are still clueless.
3. They are supposed to help you solve your
problems, but half the time they ARE the problem.
4. As soon as you commit to one, you realize
that, if you had waited a little longer, you could have had a better model.
Report TOU ViolationShare This Post
 Public ReplyPrvt ReplyMark as Last ReadFilePrevious 10Next 10PreviousNext