Is It Plagiarism... Or Is It Wikipedia-Like Collaboration?

from the rethinking-plagiarism dept

Over the past few years, we've been forced to start rethinking the concept of plagiarism quite a bit, with help from folks like Malcolm Gladwell and Jonathan Lethem, who have both started to notice that creative works tend to build on those who came before -- and that derivative works can often be an artform by themselves, if not an inspiration for additional creative works. Still, there is something of a kneejerk reaction to the idea that plagiarism is bad -- potentially for very good reasons. Passing off someone else's work as your own isn't particularly nice and from a social standpoint, getting caught doing so can really damage someone's reputation. One of the biggest concerns these days, not surprisingly, is the increasing claims that students plagiarize all sorts of things from the internet -- even to the point where some feel that children today don't even feel that it's wrong to simply pass someone else's work off as their own.

A new study looking at "personal essays" written for university admission supports this theory by pointing out repeated examples of plagiarism, where applicants pretty clearly took "personal essays" that were from certain websites and used the ideas and personal experiences in them as their own. One of the most popular, apparently, was an essay about a fascination for chemistry that began with the applicant setting fire to his or her pajamas at age 8. Apparently, that particular scenario happened to 234 individuals... Or, more likely, just one of them, and the rest took the idea from the fact that the essay was posted to a site showing "successful" personal statements. Most of the essays weren't plagiarized directly -- they just built on the idea. Of course, rather than just condemning the concept, Jeremy Wagstaff has a very interesting observation. He suggests that perhaps many of the applicants don't think of it as "plagiarism" but more like wiki-style collaboration. That is, they've grown up in an age of internet collaboration where no one person "owns" the content, but that content is an ongoing process of ideas that anyone can participate in. In such a world, the idea of "plagiarism" has little meaning. Adding a paragraph to a Wikipedia entry isn't plagiarizing the rest of the entry.

Of course, some Wikipedia detractors may find this to be yet another troublesome sign -- that Wikipedia is teaching children to plagiarize. However, a more reasonable way of looking at it, is that it's teaching students the value of collaborative work, and building on the ideas of those who have come before them. That's a valuable lesson. None of this, of course, excuses passing off someone else's work as your own -- especially in a situation like a personal statement to gain admission into a university. However, it could help to explain the issues of plagiarism in students that shows it's not all about just getting off easy by copying content, and more about a more collaborative approach to content. If that's the case, the response shouldn't be to focus on the moral or ethical issues of "copying," but simply doing a better job of teaching students the borderline between collaborative work and independent work.
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  1. identicon
    misanthropic humanist, 11 Mar 2007 @ 11:25am

    Re: Re: myth of originality

    Very interesting Hua, I think your work has the right spirit. I know this domain by some different terminology which I hope you can understand. A lot of what you discuss falls into the field I know of as "expert systems" and "knowledge based systems", ES & KBS.

    The problems that arise, for a computational solution to such a search problem, are the brittleness of natural language codification and parsing, and the problem of eliminating circualrity.

    Let's avoid the first of those, because we could discuss it all day long :)

    Circularity is more troubling in many ways. Many things in human knowledge are defined in terms of themselves, for example the negative integers, commutative and distributive laws of mathematics and the nature of multiplication and addition are all tied up in the same nest. That doesn't mean they are wrong or of no utility. But it does mean they can't be represented in anything which is equivilent to a tree (non-cyclic graph). That in turn causes many problems for computers.

    If you are considering only facts, let's say football results, then no problem. Expert systems already shine when dealing with this kind of data. But consider medical expert systems for diagnosis of complicated disease. There may be omissions, inaccuracies and sometimes pieces of sub-problem knowledge that are simply wrong.
    This isn't intractable in itself, you can use backtracking and fuzzy forms of predicate logic that can help you route around partial data. But, finally there is a class of KB problems that are "strange", not merely fuzzy. For example mathematical questions like the number theory example I just gave you. How would you decide who first "discovered" disjoint space, as it applies to set theory (union, intersect), logic (and, or) and arithmetic (add, multiply). Those are all equivilences of the same concept expressed in different symbolic form. In other words how can you distinguish between branches of human knowledge that appear to be disjoint but are in fact about the same thing unless you have profound understanding at the computational level? I suggest that if you have solved this you have already solved a much larger and more significant AI problem than the application you talk of.

    Philosophically, your machine that could determine if any idea is original or not would be capable of original thought, if only by random hypothesis and exhaustive search. That would probably turn a few heads in the AI field too :)

    Anyway, aim for the stars and you will achieve much even if you fall terribly short.

    Btw, your description of the book conundrum has many resonances of Searls "Chinese Room". Although I realise you are not proposing strong AI you have a problem still, that such a machine could only be trusted, but never proven. It's behaviour to the outside observer may be spectacularly accurate and consistent, but it would only ever be "black box" behaviour. That would have certain, er legal ramifications, to say the least :) I am impressed that you get this and arrive at the idea of internalised "experts" each with a with simultaneous hypothesis that cannot be reconciled, but can at least be given as output to be examined by a human. Have you studied automatic theorem proving? I think you would get a lot of great insights to spur your work on by reading this subject. You will see that many assumptions you make about comutability are much more difficult than they first seem.

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