Plagiarism detection – quality management tool for all scientific journals

Baždarić, Ksenija

doi:10.3325/cmj.2012.53.1

Cited by 35 publications

(21 citation statements)

References 12 publications

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“…Many research institutions and universities today have endorsed principles and standards for responsible conduct of research, for example by endorsing definitions of plagiarism, producing guidelines and policies to handle allegations, and organizing courses on how to prevent and detect plagiarism (Antes et al 2009;Resnik and Zeng 2010;Resnik and Master 2013;Godecharle et al 2013). Scientific journals have also been increasingly proactive against plagiarism, through the establishment of clear policies, guidelines and the pre-emptive use of text-matching software (Bosch et al 2012;Supak-Smolcic and Simundic 2013;Baždarić 2012;Butler 2010;Horrom 2012;Martin 2013).…”

Section: Introductionmentioning

confidence: 98%

Scientists Admitting to Plagiarism: A Meta-analysis of Surveys

2014

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We conducted a systematic review and meta-analysis of anonymous surveys asking scientists whether they ever committed various forms of plagiarism. From May to December 2011 we searched 35 bibliographic databases, five grey literature databases and hand searched nine journals for potentially relevant studies. We included surveys that asked scientists if, in a given recall period, they had committed or knew of a colleague who committed plagiarism, and from each survey extracted the proportion of those who reported at least one case. Studies that focused on academic (i.e. student) plagiarism were excluded. Literature searches returned 12,460 titles from which 17 relevant survey studies were identified. Meta-analysis of studies reporting committed (N = 7) and witnessed (N = 11) plagiarism yielded a pooled estimate of, respectively, 1.7% (95% CI 1.2-2.4) and 30% (95% CI 17-46). Basic methodological factors, including sample size, year of survey, delivery method and whether survey questions were explicit rather than indirect made a significant difference on survey results. Even after controlling for these methodological factors, between-study differences in admission rates were significantly above those expected by sampling error alone and remained largely unexplained. Despite several limitations of the data and of this meta-analysis, we draw three robust conclusions: (1) The rate at which scientists report knowing a colleague who committed plagiarism is higher than for data fabrication and falsification; (2) The rate at which scientists report knowing a colleague who committed plagiarism is correlated to that of fabrication and falsification; (3) The rate at which scientists admit having committed either form of misconduct (i.e. fabrication, falsification and plagiarism) in surveys has declined over time.

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Section: Introductionmentioning

confidence: 98%

Scientists Admitting to Plagiarism: A Meta-analysis of Surveys

2014

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“…This condition shows that similarity is not a significant way to determine reduplication, though it is still can be used as the first filter before doing similarity detection on a written discourse. Based on Bazdaric assumption [16], plagiarism in a piece of writing is estimated to have the range of 5-10 % similarity or around 100 words similar in one document, so we also have to pay attention on the size of the document checked. From the overall result showed in Figure 5, text preprocessing on similarity detection that combines stemming and stopword gives the highest accuracy value to all word gram level that applied in 2 scenarios which are evaluated with the rate of F 1 number is 42%.…”

Section: Resultsmentioning

confidence: 99%

Stemming Influence on Similarity Detection of Abstract Written in Indonesia

2016

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“…If text similarity is present in certain sections of manuscripts, where it can be fixed or it does not invalidate the work done, we believe a second opportunity should be provided, especially in the case of inexperienced authors whose writing skills are not scientifically appreciable. 3,5 Hence, we propose that decisions should be made after manual re-evaluation rather than based solely on the automated plagiarism report.…”

Section: Discussionmentioning

confidence: 99%

Google versus other text similarity tools in detection of plagiarism: a pilot study in the Journal of Clinical and Diagnostic Research

2016

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A study conducted by Baždarić and colleagues 4 in 2012 showed that 14% of manuscripts submitted to the Croatian Medical Journal were suspected of plagiarism. However, on manual verification, 11% of these submissions were considered plagiarised. Therefore, to avoid false positive results, a manual verification by an editor is required.We practised using CrossCheck (iThenticate) in JCDR, to check for plagiarism, but after a few instances where we could identify misses, we re-evaluated our strategy. Later, we started to manually search for similar sentences using Google. We conceptualised a study to bring out any significant gain that we might have had by this change in our practice against the increased manual work. For the completion of the study we considered eight software programmes, 2 ran a feasibility test and found that three of them were easily assessable and usable. Our primary aim was to compare three text similarity programmes (iThenticate, Plagiarism Checker X, and Viper) against Google in detecting text similarity in our journal. MethodsSince 2014, we have employed an in-house staff member to check for plagiarism using Google. The staff member was asked to provide all manuscripts, submitted from 1st January to 31st May, 2015, which were flagged during the initial screenings with markedly high text similarity.JCDR received 1700 manuscripts in this time period. In 96 manuscripts, the decision was greatly affected due to the presence of text similarity. From these, 25 manuscripts (16 original manuscripts and 9 case reports) were randomly selected. We do not usually quantify the text similarity into percentages. The chunks of similar text are marked and the URL links of the source are tagged along the manuscript. In certain cases, the editor rejects a manuscript exclusively based on this so-called Google report and a review report where the reviewer advises rejection of the manuscript. In both situations, the text similarity is considered as an alarm for the decision on the manuscript-either major revision or rejection. The editorial policy is to remove the text similarities by rephrasing the sentences and citing the references, if the original article or case-report quality of data outweighs the draft originality. AbstractBackground: We practised using plagiarism detection software in the Journal of Clinical and Diagnostic Research, but after a few significant items were missed, we re-assessed our strategy and compared Google with three other text similarity programmes.Method: 25 manuscripts (16 original articles and 9 case reports) were randomly selected, where the decision to publish had been greatly affected by plagiarism. These manuscripts were checked for plagiarism, searching each sentence using Google. The same manuscripts were run through three text similarity software programmes (iThenticate, Viper, and Plagiarism Checker X). For original research, we considered methodology, results, and discussion; and for case reports, we considered case details and discussion. Each report was checked by the i...

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Plagiarism detection – quality management tool for all scientific journals

Cited by 35 publications

References 12 publications

Scientists Admitting to Plagiarism: A Meta-analysis of Surveys

Scientists Admitting to Plagiarism: A Meta-analysis of Surveys

Stemming Influence on Similarity Detection of Abstract Written in Indonesia

Google versus other text similarity tools in detection of plagiarism: a pilot study in the Journal of Clinical and Diagnostic Research

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