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A recent study warns policy-makers in developing countries about the poor quality of research evaluation. The findings show the infiltration of journals suspected of predatory practices into the citation database Scopus. 324 journals that appear both in Beall’s lists and on Scopus and 164,000 articles published from 2015-17 were identified. As a result, the Scopus Content Selection and Advisory Board removed underperforming journals.

Two Ho Chi Min City university academics have lost their positions after they were found plagiarising content from an international peer-reviewed journal. They confessed that for one of their published articles, they had translated 80% of the original article without getting the author’s permission or citing his work as the source.

The thesis of the former German minister for family affairs was found to contain nearly 70 passages violating good academic practice. The Free University of Berlin required the minister to return the PhD certificate within one month after notification of the withdrawal of the degree, acquired through “deceit regarding independent academic achievement”.

A recent 2021 study shows that academics from the Organisation of Islamic Cooperation (OIC) countries accounted for 17 of the top 20 countries where academics publish in predatory journals. The OIC’s share of global publications has increased to over 8% in 2018. Standards have been compromised in relation to the impact of the number of published papers and citations on staffing, promotions, careers, and benefits.

A Statistics Canada study shows that one-third of women and nearly a quarter of men who teach and conduct research in Canadian university and college campuses face harassment. Data collected in late 2019 from 27,000 respondents about their experiences over the past year revealed five types of harassment: verbal abuse, humiliating behaviour, threats such as blackmail, threats to career or reputation, physical threats, physical violence, and unwanted sexual attention or sexual harassment.

A research team from the Canadian University of Dubai has identified a new approach to combating academic integrity using machine learning techniques. The new method applies an algorithm to identify cases of cheating through post-exam analysis of student grades from quizzes, midterm exams, and the final exam of an entire class. This technique can complement the work of commercial plagiarism detection software and provide a deterrent and non-intrusive alternative to remotely proctored exams.