A man owned a small farm. The Labor Department determined he was not paying proper wages to his staff and sent an investigator out to interview him.

"I need a list of your employees and how much you pay them!", demanded the investigator.

"Well," replied the farmer, "there's my farm hand who's been with me for three years. I pay him $200 a week plus free room and board.

"The cook has been here for 18 months, and I pay her $150 per week plus free room and board."

"Then there's the halfwit. He works about 18 hours every day and does about 90% of all the work around here. He makes about $10 a week. He pays his own room and board, and I buy him a bottle of whiskey every Saturday night. He also sleeps with my wife occasionally."

"That's the guy I want to talk to...the halfwit!" said the agent.

A language instructor was explaining to her class that in French, nouns unlike their English counterparts, are grammatically designated as masculine or feminine. "House," in French, is feminine - "la maison." "Pencil," in French, is masculine - "le crayon."

One puzzled student asked, "What gender is the computer?" The teacher did not know, and the word wasn't in her French dictionary. So for fun she split the class into two groups, appropriately enough by gender, and asked them to decide whether "computer" should be a masculine or feminine noun.

Both groups were required to give four reasons for their recommendation.

The men's group decided that computers should definitely be of the feminine gender ("la computer"), 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 possible later retrieval.

4. As soon as you make a commitment to one, you find yourself spending half your pay check on accessories for it.

The women's group, however, concluded that computers should be masculine ("le computer"), because:

1. In order to get their attention, you have to turn them on.

2. They have a lot of data but they are still clueless.

3. They are supposed to help you solve problems, but half the time they ARE THE PROBLEM.

4. As soon as you commit to one, you realize that if you'd waited a little longer, you could have gotten a better model.