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Animal Ethics Biosafety Human Research Ethics Research Integrity

The use of text-matching software’s similarity scores – Papers (Stewart Manley | October 2021)

Posted by Dr Gary Allen in Research Integrity on April 15, 2022
Keywords: Authorship, Institutional responsibilities, Journal, Publication ethics, Research Misconduct, Research results, Researcher responsibilities

The Linked Original Item was Posted On October, 17 2021

Businessman hand writing sign stop plagiarism, Business concept.

This open access paper, published in October 2021, looks at the overuse of “similarity score” or “Similarity Index” to detect and quantify plagiarism (for institutional investigations), reflects upon their failings and urges for a greater use of the more detailed Similarity Reports. We have included links to four related items.

ABSTRACT
Popular text-matching software generates a percentage of similarity – called a “similarity score” or “Similarity Index” – that quantifies the matching text between a particular manuscript and content in the software’s archives, on the Internet and in electronic databases. Many evaluators rely on these simple figures as a proxy for plagiarism and thus avoid the burdensome task of inspecting the longer, detailed Similarity Reports. Yet similarity scores, though alluringly straightforward, are never enough to judge the presence (or absence) of plagiarism. Ideally, evaluators should always examine the Similarity Reports. Given the persistent use of simplistic similarity score thresholds at some academic journals and educational institutions, however, and the time that can be saved by relying on the scores, a method is arguably needed that encourages examining the Similarity Reports but still also allows evaluators to rely on the scores in some instances. This article proposes a four-band method to accomplish this. Used together, the bands oblige evaluators to acknowledge the risk of relying on the similarity scores yet still allow them to ultimately determine whether they wish to accept that risk. The bands – for most rigor, high rigor, moderate rigor and less rigor – should be tailored to an evaluator’s particular needs.

KEYWORDS:
Plagiarism, text-matching software, accountability in research, similarity score, iThenticate, Turnitin, publication ethics

Stewart, M. (2021) The use of text-matching software’s similarity scores. Accountability in Research, DOI: 10.1080/08989621.2021.1986018
Publisher (Open Access): https://www.tandfonline.com/eprint/2HYYYH7B3VYUQFBTGPFY/full?target=10.1080/08989621.2021.1986018

The use of text-matching software’s similarity scores
(2021). The use of text-matching software’s similarity scores. Accountability in Research. Ahead of Print.

Related Reading

(US) Self-Plagiarism, Fraud and iThenticate: A Complicated Relationship – Inside Higher Ed (Cary Moskovitz and Aaron Colton | March 2021)

Plagiarism detection: Perils and pitfalls (Editorial: Amitav Banerjee | October 2019)

Testing of Support Tools for Plagiarism Detection (Papers: Tomáš Foltýnek, et al | February 2020)

Interventions to prevent misconduct and promote integrity in research and publication (Review) (Papers: Ana Marusic et al 2016)

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