Similarly, acceptable thresholds for one type of article may not be appropriate for another: Review articles could be expected to have a higher overall similarity score than original research articles. A match in the discussion or conclusions with no appropriate citation, on the other hand, could set alarm bells ringing even though it only accounts for a small percentage of the manuscript. For example, editors in certain subject areas may be less concerned about sizable matches in methods sections, where there are only so many ways to describe a certain process. Secondly, where the match appears can sometimes be more important than how big the match is. Of course, a paper with six separate matches of 5% could well be as problematic as one that has copied 30% of its content from a single source, but it’s impossible to tell whether this is the case without looking at the reports. It is possible a 30% score will turn out to be a 30% match to one source, but it’s much more likely that when you look at the reports you’ll find the 30% is made up of a number of smaller matches, the largest of which might be just 4 or 5%. This is probably going to be made up of a number of smaller matches. Well, there are a number of factors that need to be taken into account when assessing a paper’s overall similarity score.įirstly, it’s important to note the similarity score is telling you the total amount of matching text. Beyond that, the score itself doesn’t give you definitive answers and definitely cannot tell you whether you have a case of plagiarism. The similarity score gives you a rough ‘headline’ that ensures heavily duplicated papers are brought straight to your attention and allows you to quickly disregard papers with hardly any matches. The answer to this question is there is no such thing as a ‘magic number’ that will tell you whether a document contains problematic content. The similarity score is the first thing you see when a document is processed and, because it’s easy to focus on this number as signifying a problem, a common question new users of the system ask is ‘what level of similarity score indicates a problem?’ When a manuscript is first uploaded to iThenticate, a Similarity Score is returned indicating the percentage of text in the uploaded document that matches text in other published documents or web pages.
CrossCheck members use the iThenticate plagiarism detection system to screen submitted papers for originality and can quickly tell whether a paper contains passages of text that also appear in other publications or resources.
Written by Kirsty Meddings, Product Manager at CrossRef - CrossCheck, the plagiarism screening initiative from CrossRef and iParadigms has recently welcomed its 240th publisher and is becoming an established part of the editorial process for many journals. CrossCheck members use the iThenticate plagiarism detection system to screen submitted papers for originality and can quickly tell whether a paper contains passages of text that also appear in other publications or resources. CrossRef and iParadigms has recently welcomed its 240th publisher and is becoming an established part of the editorial process for many journals.
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