The use of text-matching software’s similarity scores

Manley, Stewart

doi:10.1080/08989621.2021.1986018

Cited by 9 publications

(7 citation statements)

References 62 publications

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“…As pointed out by other researchers, text similarity does not necessarily mean plagiarism of text. 12 13 14 I do believe that it is naïve to just rely on a simple score to determine if an author has tried to intentionally deceive us and label a submitted manuscript plagiarized. As a principal of good editorial practice, I believe, it is a moral duty of the editorial staff to evaluate the suspicious similar text with scrutiny to determine if the similar text is really an instance of verbatim or not.…”

Section: Discussionmentioning

confidence: 99%

The Acceptable Text Similarity Level in Manuscripts Submitted to Scientific Journals

Habibzadeh¹

2023

J Korean Med Sci

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Plagiarism is among commonly identified scientific misconducts in submitted manuscripts. Some journals routinely check the level of text similarity in the submitted manuscripts at the time of submission and reject the submission on the fly if the text similarity score exceeds a set cut-off value ( e.g. , 20%). Herein, I present a manuscript with 32% text similarity, yet without any instances of text plagiarism. This underlines the fact that text similarity is not necessarily tantamount to text plagiarism. Every instance of text similarity should be examined with scrutiny by a trained person in the editorial office. A high text similarity score does not always imply plagiarism; a low score, on the other hand, does not guarantee absence of plagiarism. There is no cut-off for text similarity to imply text plagiarism.

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Section: Discussionmentioning

confidence: 99%

The Acceptable Text Similarity Level in Manuscripts Submitted to Scientific Journals

Habibzadeh¹

2023

J Korean Med Sci

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“…Most studies that estimate plagiarism based on text-matching software and additional human verification have analyzed a substantially small number of papers, fewer than 1000 (Pupovac, 2021 ). One possible way to deal with false positives is to use a percentage similarity cut‐off as a threshold, a process broadly implemented by academic journals that receive a significant number of submissions (Manley, 2021 ). As such, a high value of textual similarity, i.e., more than 50% of textual overlap, helps to eliminate false positives (Pupovac, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Ambiguity in Ethical Standards: Global Versus Local Science in Explaining Academic Plagiarism

Guba,

Tsivinskaya

2024

Sci Eng Ethics

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The past decade has seen extensive research carried out on the systematic causes of research misconduct. Simultaneously, less attention has been paid to the variation in academic misconduct between research fields, as most empirical studies focus on one particular discipline. We propose that academic discipline is one of several systematic factors that might contribute to academic misbehavior. Drawing on a neo-institutional approach, we argue that in the developing countries, the norm of textual originality has not drawn equal support across different research fields depending on its level of internationalization. Using plagiarism detection software, we analyzed 2,405 doctoral dissertations randomly selected from all dissertations defended in Russia between 2007 and 2015. We measured the globalization of each academic discipline by calculating the share of publications indexed in the global citation database in relation to overall output. Our results showed that, with an average share of detected borrowings of over 19%, the incidence of plagiarism in Russia is remarkably higher than in Western countries. Overall, disciplines closely follow the pattern of higher globalization associated with a lower percentage of borrowed text. We also found that plagiarism is less prevalent at research-oriented institutions supporting global ethical standards. Our findings suggest that it might be misleading to measure the prevalence of academic misconduct in developing countries without paying attention to variations at the disciplinary level.

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“…In 2023, a substantial number of scholarly articles (e.g. Bishop, 2023;Chan & Hu, 2023;Chan & Tsi, 2023;Chen, 2023;Cotton et al, 2023;Dergaa et al, 2023;Fitria, 2023;Huang & Tan, 2023;Khalil & Er, 2023;Limna et al, 2023;Manley, 2023;Möller, 2023;Perkins, 2023;Rudolph et al, 2023a;Shen et al, 2023;Zhou et al, 2023) have been published, delving into the application of AI, specifically ChatGPT, in aiding students with their writing endeavors. This surge of scholarly contributions underlines the significance and relevance of the subject matter under scrutiny.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proliferation of AI-powered tools has considerably widened the array of opportunities for students to engage in academic dishonesty. As Manley (2023) asserts, the accessibility of essay mills, paraphrasing software, and other AI-driven resources has facilitated plagiarism and the creation of ostensibly original content by students. Consequently, educators confront the arduous task of identifying and addressing instances of academic misconduct that are growing increasingly sophisticated and evasive.…”

Section: Literature Reviewmentioning

confidence: 99%

Teachers’ reflections on academic dishonesty in EFL students’ writings in the era of artificial intelligence

2023

JALT

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The use of text-matching software’s similarity scores

Cited by 9 publications

References 62 publications

The Acceptable Text Similarity Level in Manuscripts Submitted to Scientific Journals

The Acceptable Text Similarity Level in Manuscripts Submitted to Scientific Journals

Ambiguity in Ethical Standards: Global Versus Local Science in Explaining Academic Plagiarism

Teachers’ reflections on academic dishonesty in EFL students’ writings in the era of artificial intelligence

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