Slashdot Mirror
Crackpot Scandal In Mathematics
ocean_soul writes "It is well known among scientists that the impact factor of a scientific journal is not always a good indicator of the quality of the papers in the journal. An extreme example of this was recently uncovered in mathematics. The scandal is about one El Naschie, editor in chief of the 'scientific' journal Chaos, Solitons and Fractals, published by Elsevier. This is one of the highest impact factor journals in mathematics, but the quality of the papers in it is extremely poor. The journal has also published 322 papers with El Naschie as (co-)author, five of them in the latest issue. Like many crackpots, El Nashie has a kind of cult around him, with another journal devoted to praising his greatness. There was also a discussion about the Wikipedia entry for El Naschie, which was supposedly written by one of his followers. When it was deleted by Wikipedia, they even threatened legal actions (which never materialized)."
The harm, I think, is that he's not a well-enough-known crackpot; a respectable publisher (Elsevier) has given him a journal as his own private playground. This makes it more difficult for non-crackpots trying to enter the field (e.g. grad students) to sort the wheat from the chaff. It also allows other crackpots to come off as more credible by citing crackpot articles which have a veneer of respectability. Imagine if a computer science "journal" based on Hollywood's portrayal of how computers work were being published by the ACM, and you have some idea of how big a problem this is.
The correlation between ignorance of statistics and using "correlation is not causation" as an argument is close to 1.
If you want to automatically determine what constitutes a good journal purely from data, the definition is something like: is frequently cited by other good journals. Obviously, there's a circularity there. Various techniques attempt to mitigate it, but none are perfect, and indeed most are rather simplistic and easy to game. It's basically hard to distinguish, purely from citation data, a vibrant community of legitimate research from a vibrant community of crackpots.
In real life, most academics get around the circularity problem by starting with a set of "known good" journals that are determined by consensus in the field rather than algorithms (though this may sometimes be controversial). That lets them take into account more subjective things such as status of a research community (crackpots or not?). For example, as the linked article points out, the Annals of Mathematics is generally accepted as a top-quality venue for mathematics.
If you wanted, you could then construct an Annals-centric view of mathematical impact automatically by seeing how frequently other journals are cited by papers in Annals. This is what happens informally as journals gain and lose reputation: a promising new venue often first comes to a community's attention because its articles begin to be cited in "known good" journals.
But just taking all journals with no starting point, and attempting to extract from the citation graph which ones are "good" purely from the links, is doomed to failure, because there just isn't enough information in there to make the distinctions people want to make.
10 PRINT CHR$(205.5+RND(1)); : GOTO 10
This has been a fascinating case of Crackpottery. Read the blog and the subsequent replies. El Naschie seems to make it (Quantum Mechanical babble-speak) up as he goes along ,but unless you are an expert in this area, as Dr. John Baez is, it would be difficult for the casual reader to discern this. This is similar to the Bogdanov affair, another well know scientific scam. ( http://en.wikipedia.org/wiki/Bogdanov_Affair )I'm a little surprised it took this long for Slashdot to discover this one.
One other thing: One of Baez's beefs among others is that this bogus El Naschie journal is bundled with more respectable journals and Elsevier profits from the bogus science.
Glad to be of service.
You realize, of course, that the only reason I was able to use a computer analogy is that we're talking about pure math. If we had been talking about CS, I'd have had to go with a car analogy right off the bat.
The correlation between ignorance of statistics and using "correlation is not causation" as an argument is close to 1.
And it gets worse when money becomes involved. Pseudoscientists and crackpots often try to find "investors" for their schemes, and even a layman who performs due diligence can be fooled when publishers like Elsevier become enablers for pseudoscience. When the paper shows up in an INSPEC or Web of Science search, how is the person being scammed supposed to know that the paper isn't really legitimate?
Many "free energy" scam artists already have patents for their nonsensical inventions, thanks to the laxity of the USPTO. It'll get worse unless these "pseudo-journals" are exposed and publicized to the greater science and engineering community, as well as the public at large. I had never heard of El Naschie before today, because I'm not a mathematician; thanks to this article, more people like me will now keep an eye out for his future "work".
People used to say about a mathematician or physicist that "what he is doing is so important that only a few people in the world can understand what he is talking about."
In a few cases it was actually true.
Also, there were mathematicians who believed that the highest form of mathematics was work that had no practical application. There was a story that the inventor of matrix theory expressed pride that he had invented a form of mathematics with absolutely no practical use. Little did he know how extensively his work could be used. He would have been appalled.
There still seems to be a feeling that the less people are able to understand a paper in a math journal, the more important the paper is likely to be.
At one time I was a subscriber to the Annals of Mathematical Statistics. Papers in math journals usually assume that you know every paper previously written by the author and the others in the field. There is often very little introductory material and no tutorial material in these papers. Even if you have a general understanding of the topic, you can't follow the papers because they are written very concisely, and assume that nothing needs to be explained if it was ever published anywhere else. You may have to backtrack for years of someone's papers and still not be able to understand the paper you are trying to read.
This is probably a combined consequence of "publish or perish" in academia and page limits in journals. It is often hard to tell if a given paper makes any sense or is useful.
I guess you could call it job security through obscurity.