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Re: (off-topic) self-plagiarism



Dear Ramin,

I think yours is the most sensible comment so far on the subject of
multiple publications of the same material.  Thank you for your
fair-mindedness.

Al
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Albert S. Bregman, Emeritus Professor
Psychology Department, McGill University
1205 Docteur Penfield Avenue
Montreal, QC, Canada H3A 1B1.
Office:  Phone: (514) 398-6103
              Fax: (514) 398-4896
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2009/7/10 Ramin Pichevar <Ramin.Pichevar@xxxxxxxxxxxxxx>:
> Hi Joe,
> I think you've got a very valid point here. I think the best way would be to
> share your and other reviewers' concerns with the authors through the editor
> and ask them to further explain and clarify the matter. In any fair judiciary
> system, people are allowed to defend themselves. I don't see why this shouldn't
> be the case in the scientific review process. The editor and the reviewers can
> then take corrective actions (if necessary) based on the authors'
> clarifications.
> Regards,
> Ramin
> Quoting Joe Sollini <joe@xxxxxxxxxxxxx>:
>
>> Unfortunately I couldn't find the other four.  Of the two I have read he
>> does use actual data in one, but as you rightly point in the other he talks
>> about it's application but does not actually model any data.  It sounds like
>> you have a very good case to suggest this is repetition (self-plagiarism).
>> I share your disbelief at how this has happened, it's possibly due to the
>> shifts in domain/scientific fields that this model traverses.  Although
>> given you found 6 with a google search of the title this shouldn't be to
>> much of a barrier.
>>
>> Joe
>>
>> -----Original Message-----
>> From: AUDITORY - Research in Auditory Perception
>> [mailto:AUDITORY@xxxxxxxxxxxxxxx] On Behalf Of Laszlo Toth
>> Sent: 10 July 2009 13:27
>> To: AUDITORY@xxxxxxxxxxxxxxx
>> Subject: Re: (off-topic) self-plagiarism
>>
>> On Fri, 10 Jul 2009, Joe Sollini wrote:
>>
>> > Sorry to bring this up again but having had a look for through these
>> papers
>> > Instead of finding six papers I was only able to find 3 (but six links
>> > to papers).
>>
>> I found seven papers with virtually the same abstract (the one I received
>> for review is the 8th). Unfortunately, I have access to the full text only
>> in 3 cases (plus the one for review, but I keep that one in secret...), so
>> this is why I have to judge based mainly on the abstracts.
>> (I can send you a list, maybe you can help me get the remaining ones.)
>>
>> > If he has a model with a wide range of applications and
>> > applies this model to fields as disparate as face recognition and virology
>> > it could perhaps be deemed as fitting that they need to be published in
>> > journals that people practising in the respective fields read?
>>
>> I definitely agree with that. But this would require the content of the
>> paper to be:
>> 1. We claim that we have a new theoretical model and that it is
>> applicable to the field of the journal (conference).
>> 2. The description of the newly proposed model.
>> 3. Empirical justification on data taken from the specific field.
>>
>> This doesn't hold in this case, as I'll explain below.
>> >From the 7 paper titles of 3 says "a fast ... model", and 4 ones go like
>> "a fast model applied to the field of ...". So, the titles themselves accord
>> with your description above. However, let's move on to the abstracts.
>> >From the 7 abstracts 5 starts with the sentence: "This paper
>> presents a new approach to speed up the operation of <model>".
>> So the topic of the papers (according to the abstract) is NOT the
>> application of the model to a new domain, but a theoretical result on how
>> to compute it faster than earlier. Although the other two abstracts start
>> with "this paper presents an intelligent approach to detect...",
>> the remaining text is the same in all seven cases: "it is proved
>> theoretically and practically that the number of computation required
>> <by the new method> is less than that needed by the <old method>. So
>> although the paper titles claim the the method will be tested on a new
>> domain, there is no word about that in the abstract!
>> Theoretical chapter of the three papers: these are word-by-word the same
>> in the 3 papers I have access to. Notice again that the formulas are about
>> the speed-up factor (number of operations required) of the method compared
>> to the old one, so these again agree with the abstract, not the titles.
>> Now, the funniest part: the experiments. In two of the papers the numbers
>> are given in diagrams, in one in tables, so I cannot really tell if they
>> are different or the same. However, these results are clearly ALL about
>> speed-up ratios. So while the titles say that we will apply the method to
>> a new domain (virus detection, code detection, record detection, etc.).
>> there are NO detection results given at all! Just speed-up results,
>> (as promised by the abstract). No proof that it works, only proof that it
>> can be faster than before. And the most shocking part: none of the papers
>> says ANYTHING about the test data! Only that these are Matlab simulations.
>> But it is not stated at all that the data were domain-specific. I simply
>> can't believe that these went through a review process. Ah, and finally, the
>> Conclusions: its again the same in all papers, stating that "computations
>> have shown that <new model> requires fewer compuation stepts that <old
>> model>". Which is true, but thas nothing to do with the claim of the
>> titles that the model will be applied to a new domain.
>> Again, I can't say anything about the remaining 4 papers, but based on
>> their abstracts I suspect that they were also "generated" with the "let's
>> adjust the title and the first sentence to the domain" method. (which,
>> as I said, would be acceptable if the experiments were also adjusted...)
>>
>>                Laszlo Toth
>>         Hungarian Academy of Sciences         *
>>   Research Group on Artificial Intelligence   *   "Failure only begins
>>      e-mail: tothl@xxxxxxxxxxxxxxx            *    when you stop trying"
>>      http://www.inf.u-szeged.hu/~tothl        *
>>
>>
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>>
>



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