Author:
Kopotev Mikhail,Rostovtsev Andrey,Sokolov Mikhail
Abstract
AbstractThis chapter describes how academic plagiarism poses a challenge for digital humanities, when sophisticated tools make it possible to discover inappropriate academic activity. Focusing on dissertations defended in Russia in recent years, the authors discuss academic plagiarism and examine the changing norms of academic integrity. Section 27.1 introduces the questions under consideration. The next describes various types of plagiarism and computational tools used to detect them. Section 27.3 reviews available digitized resources. The activities of the Dissernet network are described in Sect. 27.4, which presents an overall picture of findings based on large-scale (more than 50%) plagiarism in dissertations. The case study described in Sect. 27.5 concerns small-scale plagiarism within the same academic genre, raising the question of academic authenticity’s shifting norms.
Publisher
Springer International Publishing
Reference25 articles.
1. Acuna, D. E., P. S. Brookes, and K. P. Kording. 2018. Bioscience-scale Automated Detection of Figure Element Reuse. Preprint at bioRxiv. Accessed February 1, 2019. https://doi.org/10.1101/269415.
2. Barthes, R. 1968. La mort de l’auteur. Manteia 5: 12–17.
3. Clauset, Aaron, Samuel Arbesman, and Daniel B. Larremore. 2015. Systematic Inequality and Hierarchy in Faculty Hiring Networks. Science Advances 1 (1): e1400005.
4. Denisova-Schmidt, E. 2016. Corruption in Russian Higher Education. Russian Analytical Digest 191: 5–9.
5. Eisa, Taiseer, Naomie Salim, and Salha Alzahrani. 2015. Existing Plagiarism Detection Techniques: A Systematic Mapping of the Scholarly Literature. Online Information Review 39: 383–400. https://doi.org/10.1108/OIR-12-2014-0315.
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献