Computergeschlecht

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Frauen neigen dazu, ihn als Mann zu sehen, denn:

• Computer sind voller Informationen und haben doch keine Ahnung.
• Sie sollten eigentlich Probleme lösen und sind doch die meiste Zeit selbst das größte Problem.
• Um ihre Aufmerksamkeit zu erregen, muß man sie anmachen.
• Sobald man sich für einen entschieden hat, stellt sich heraus, daß man nur kurz auf ein besseres Modell hätte warten müssen.

Männer hingegen plädieren für weibliche Computer:

• Niemand außer ihrem Schöpfer versteht ihre Logik.
• Die Sprache, mit der sie sich untereinander unterhalten, versteht kein anderer.
• Sie vergessen nie auch nur den kleinsten Fehler.
• Kaum hat man sich für ein Modell entschieden, muß man ständig für zusätzliche Ausstattungen aufkommen. 

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 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.