Content - Gamna
Translator's note: I already wrote about media filtering. This article is written by renowned economist Arnold Kling from George Mason University. Despite his authority, the author used a very bold comparison in the article. Let it not mislead you. The article was published on January 13, 2003. Now, more than 6 years later, in the conditions of the media crisis, it is especially noteworthy. I did not come across her translations into Russian. Some materials that I will publish further will be understood better only after reading this article.
“So this week we can be happy to launch on Monday some interesting software from the organization at Stanford University, which is called “ Creative Commons ” ( Creative Commons )”, - Dan Gillmor.
Creative Comons is an internet service founded by Lawrence Lessig . Lessig is a lawyer and publicist specializing in the legal aspects of the Internet. The service allows the creator of music, text or software to put a label on the content that describes the rules for its use.
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Although there are many comrades in the network who share Dan Gilmore's enthusiasm for “Creative Commons”, I am not infected with them. If it makes sense, it is very small, because it is based on the amazingly naive view of the 60s about how content processors function. Commons enthusiasts believe that publishers make profits by using copyright law to steal content from the creator, and setting predatory prices for the consumer.
I, on the contrary, believe that it is necessary to recognize that publishers perform a full-fledged economic function of filtering content, distributing it efficiently and selling it to consumers. I have said many times that modern media companies deserve deep contempt. Nevertheless, although we can do without the current publishers, without the function that they carry out, we can not do anything.
If you think about the sewage system, not the most pleasant comes to mind. What you flush down the toilet goes through a filter system. This system “takes care of” the stock until it can send you the contents back as drinking water.
As content processors, publishers perform a similar function. Individual programmers, writers, musicians produce something closer to washable. Computer programs, books, music that people buy are something closer to drinking water.
All you are allowed to do by “Creative Commons” is to put a label on your piece, before you drop it into the toilet. If you do not want the wastewater treatment plant to filter it and sell the water under normal conditions, Creative Commons allows you to give instructions to this account. If you think that publishers steal your din, you can stop them.
In reality, publishers are not just stealing. They create added value. They benefit by filtering content that is not claimed by people, and contain a mechanism for collecting fees and transferring deductions to authors. Those who wish to deal a serious blow to this industry should simply learn how to perform these functions more effectively and efficiently than the current players in the market.
I am very optimistic about the possibilities of the Internet about new, more efficient forms of content processing. However, the main task is to solve the problems of content filtering and earnings generation. In other words, new players must add significant value and find a way to take money for it.
The network has generated a lot of alternative publishing models, but they have not added enough cost. I believe that the main gain here will be obtained through the use of so-called Bayesian filters.
Bayesian filters are now used to combat spam. If we assume that the raw content is similar to what is drained into the sewer, it becomes clear that the problem of the publishing business is similar to the problem of spam filtering. We must learn to skip the right message and discard trash.
Bayesian filters differ from a regular filter by keyword in two ways. First, Bayesian filters use flexible word weighting, not rules for matching the stop word. Secondly, I personally can “teach” the Bayesian filter by filtering my mail myself.
For example, I have a friend in Philadelphia who writes me letters himself and sends two more types of letters - comments on the Middle East and jokes. When I receive letters from him, I look at the headline to see what kind of letter it is. I want to read his letters and comments, and remove jokes without viewing.
Keyword filters are almost useless here. Obviously, I can not apply the rule that says: "Consider all letters from this person as spam." And the Bayesian filter, which learns to weigh different factors in a letter, effectively performs this filtering. He may even be less cruel than myself. I read some uninteresting stories from the Middle East, and I lack good jokes, and the Bayesian filter can be so clever that it will find good jokes and reject the uninteresting comment.
I believe blogs perform the filtering function. But it can be improved by Bayesian filters. No matter how much I love Asymmetrical Information and Brad DeLong's Semi-Daily Journal , there are a lot of uninteresting ones for them. But you could spend time on materials of interest in other blogs. If I could teach the Bayesian filter, I would use my time more efficiently.
Even the search for Google can be improved by Bayesian filters. Every Google user enters keywords and responds to the results of the issue in his own way. It can easily happen that two different people will enter the same words, but in fact they will look for completely different things. If Google could use Bayesian filters to learn how I use his search, he could give me more relevant results.
If you are interested in what technology the future is in the field of content processing, I would put it on Bayesian filters. I wouldn't bet on “Creative Commons”.
Creative Commons is based on a naive ideology that believes that raw content is gold that is stolen by evil media companies. In fact, the economics of content is the added value that comes from filtering content, not from producing it. If you want to skip traditional publishers on the Internet, you had better start by saying that the content is gamna.
Content - Gamna.
“So this week we can be happy to launch on Monday some interesting software from the organization at Stanford University, which is called “ Creative Commons ” ( Creative Commons )”, - Dan Gillmor.
Creative Comons is an internet service founded by Lawrence Lessig . Lessig is a lawyer and publicist specializing in the legal aspects of the Internet. The service allows the creator of music, text or software to put a label on the content that describes the rules for its use.
')
Although there are many comrades in the network who share Dan Gilmore's enthusiasm for “Creative Commons”, I am not infected with them. If it makes sense, it is very small, because it is based on the amazingly naive view of the 60s about how content processors function. Commons enthusiasts believe that publishers make profits by using copyright law to steal content from the creator, and setting predatory prices for the consumer.
I, on the contrary, believe that it is necessary to recognize that publishers perform a full-fledged economic function of filtering content, distributing it efficiently and selling it to consumers. I have said many times that modern media companies deserve deep contempt. Nevertheless, although we can do without the current publishers, without the function that they carry out, we can not do anything.
Gamnostok problem
If you think about the sewage system, not the most pleasant comes to mind. What you flush down the toilet goes through a filter system. This system “takes care of” the stock until it can send you the contents back as drinking water.
As content processors, publishers perform a similar function. Individual programmers, writers, musicians produce something closer to washable. Computer programs, books, music that people buy are something closer to drinking water.
All you are allowed to do by “Creative Commons” is to put a label on your piece, before you drop it into the toilet. If you do not want the wastewater treatment plant to filter it and sell the water under normal conditions, Creative Commons allows you to give instructions to this account. If you think that publishers steal your din, you can stop them.
Bayesian converters
In reality, publishers are not just stealing. They create added value. They benefit by filtering content that is not claimed by people, and contain a mechanism for collecting fees and transferring deductions to authors. Those who wish to deal a serious blow to this industry should simply learn how to perform these functions more effectively and efficiently than the current players in the market.
I am very optimistic about the possibilities of the Internet about new, more efficient forms of content processing. However, the main task is to solve the problems of content filtering and earnings generation. In other words, new players must add significant value and find a way to take money for it.
The network has generated a lot of alternative publishing models, but they have not added enough cost. I believe that the main gain here will be obtained through the use of so-called Bayesian filters.
Bayesian filters are now used to combat spam. If we assume that the raw content is similar to what is drained into the sewer, it becomes clear that the problem of the publishing business is similar to the problem of spam filtering. We must learn to skip the right message and discard trash.
Bayesian filters differ from a regular filter by keyword in two ways. First, Bayesian filters use flexible word weighting, not rules for matching the stop word. Secondly, I personally can “teach” the Bayesian filter by filtering my mail myself.
For example, I have a friend in Philadelphia who writes me letters himself and sends two more types of letters - comments on the Middle East and jokes. When I receive letters from him, I look at the headline to see what kind of letter it is. I want to read his letters and comments, and remove jokes without viewing.
Keyword filters are almost useless here. Obviously, I can not apply the rule that says: "Consider all letters from this person as spam." And the Bayesian filter, which learns to weigh different factors in a letter, effectively performs this filtering. He may even be less cruel than myself. I read some uninteresting stories from the Middle East, and I lack good jokes, and the Bayesian filter can be so clever that it will find good jokes and reject the uninteresting comment.
I believe blogs perform the filtering function. But it can be improved by Bayesian filters. No matter how much I love Asymmetrical Information and Brad DeLong's Semi-Daily Journal , there are a lot of uninteresting ones for them. But you could spend time on materials of interest in other blogs. If I could teach the Bayesian filter, I would use my time more efficiently.
Even the search for Google can be improved by Bayesian filters. Every Google user enters keywords and responds to the results of the issue in his own way. It can easily happen that two different people will enter the same words, but in fact they will look for completely different things. If Google could use Bayesian filters to learn how I use his search, he could give me more relevant results.
Economy versus ideology
If you are interested in what technology the future is in the field of content processing, I would put it on Bayesian filters. I wouldn't bet on “Creative Commons”.
Creative Commons is based on a naive ideology that believes that raw content is gold that is stolen by evil media companies. In fact, the economics of content is the added value that comes from filtering content, not from producing it. If you want to skip traditional publishers on the Internet, you had better start by saying that the content is gamna.
Source: https://habr.com/ru/post/288196/
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