Sunday, March 11, 2018
DEREK HUNTER: To hell with California.
I remember as a kid being told California was going to fall into the ocean, be destroyed by an earthquake. If it continues down the path is heading now, there’s a chance it will be the whole country that gets destroyed.I'd prefer the earthquake. Cleaner.
If we let California go, and we split the country along ideological lines we could fend off what seems to be looming just over the horizon, which is an ugly divorce. We simply don’t get along anymore and want completely different, irreconcilable things. Manifest Destiny was a nice idea, and we made it work for a long time, but it’s heading toward not working anymore. So maybe we let California go we’d all be a lot happier, if not better off.
A COMPUTER'S GENDER
A language instructor was explaining to her class that in French, nouns unlike their English counterparts, are grammatically designated as masculine or feminine.The women won.
"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; and
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; and
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.
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