A French teacher was explaining to her class that in French, unlike English, nouns are designated as either masculine or feminine.
A student asked,
Instead of giving the answer, the teacher split the class into two groups - male and female - and asked them to decide for themselves whether "computer" should be a masculine or a feminine noun. Each group was asked to give four
reasons for their recommendation.
The men's group decided that "computer" should 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 in long-term memory for possible later review; and
4. As soon as you make a commitment to one, you find yourself spending half your salary on accessories for it.
The women's group however, concluded that computers should be masculine ("le computer") because:
1. In order to do anything with them, you have to turn them on;
2. They have a lot of data but still can't think for themselves;
3. They are supposed to help you solve problems, but half the time they ARE the
problem; and
4. As soon as you commit to one, you realize that if you had waited a little longer, you could have gotten a better model.
"House" is feminine-"la maison."
"Pencil" is masculine-"le crayon."
"What gender is 'computer'?"
reasons for their recommendation.
The men's group decided that "computer" should 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 in long-term memory for possible later review; and
4. As soon as you make a commitment to one, you find yourself spending half your salary on accessories for it.
The women's group however, concluded that computers should be masculine ("le computer") because:
1. In order to do anything with them, you have to turn them on;
2. They have a lot of data but still can't think for themselves;
3. They are supposed to help you solve problems, but half the time they ARE the
problem; and
4. As soon as you commit to one, you realize that if you had waited a little longer, you could have gotten a better model.
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