Is predatory publishing a real threat? Evidence from a large database study

Perlin, Marcelo; Imasato, Takeyoshi; Borenstein, Denis

doi:10.1007/s11192-018-2750-6

Cited by 50 publications

(35 citation statements)

References 32 publications

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“…Число хищнических журналов, определенных таким образом, выросло экспоненциально с 2010 г. [76,77]. Демонстрация МЕтодология НиР Д. П. Теннант и др., 2019, № 3, с. неэтичной практики в издательской индустрии ОД также привлекла внимание средств массовой информации [78].…”

Section: тема 5 открытый доступ ответственен за появление хищных издunclassified

Ten hot topics around scholarly publishing

et al. 2019

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Section: тема 5 открытый доступ ответственен за появление хищных издunclassified

Ten hot topics around scholarly publishing

et al. 2019

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“…Here we investigate result by result. Second, we look at the occurrence of a particular statistical error relating to a failure to adjust 4 All papers that indicated some minimal peer review process, however, we observed some outlets appear on Geoff Beall's controversial 'predatory' publisher list [16].…”

Section: Resultsmentioning

confidence: 96%

The Prevalence of Errors in Machine Learning Experiments

Shepperd

Guo

et al. 2019

Intelligent Data Engineering and Automated Learning – IDEAL 2019

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Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method : We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.

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“…On the other hand, they also list journals with subpar standards of peer review and linguistic correction [75]. The number of predatory journals thus defined has grown exponentially since 2010 [76,77]. Demonstrating unethical practices in the OA publishing industry also attracted considerable media attention [78].…”

Section: Topic 5: Is Open Access Responsible For Creating Predatory Pmentioning

confidence: 99%

Ten Hot Topics around Scholarly Publishing

et al. 2019

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The changing world of scholarly communication and the emerging new wave of ‘Open Science’ or ‘Open Research’ has brought to light a number of controversial and hotly debated topics. Evidence-based rational debate is regularly drowned out by misinformed or exaggerated rhetoric, which does not benefit the evolving system of scholarly communication. This article aims to provide a baseline evidence framework for ten of the most contested topics, in order to help frame and move forward discussions, practices, and policies. We address issues around preprints and scooping, the practice of copyright transfer, the function of peer review, predatory publishers, and the legitimacy of ‘global’ databases. These arguments and data will be a powerful tool against misinformation across wider academic research, policy and practice, and will inform changes within the rapidly evolving scholarly publishing system.

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Is predatory publishing a real threat? Evidence from a large database study

Cited by 50 publications

References 32 publications

Ten hot topics around scholarly publishing

Ten hot topics around scholarly publishing

The Prevalence of Errors in Machine Learning Experiments

Ten Hot Topics around Scholarly Publishing

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