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Who reviews for predatory journals? A study on reviewer characteristics

Severin, A.; Strinzel, M.; Egger, M.; Domingo, M.; Barros, T. F.

2020-03-11 scientific communication and education
10.1101/2020.03.09.983155 bioRxiv
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BackgroundWhile the characteristics of scholars who publish in predatory journals are relatively well-understood, nothing is known about the scholars who review for these journals. We aimed to answer the following questions: Can we observe patterns of reviewer characteristics for scholars who review for predatory journals and for legitimate journals? Second, how are reviews for potentially predatory journals distributed globally? MethodsWe matched random samples of 1,000 predatory journals and 1,000 legitimate journals of the Cabells Scholarly Analytics journal lists with the Publons database of review reports, using the Jaro-Winkler string metric. For reviewers of matched reviews, we descriptively analysed meta-data on reviewing and publishing behaviour. ResultsWe matched 183,743 unique Publons reviews that were claimed by 19,598 reviewers. 6,077 reviews were conducted for 1160 unique predatory journals (3.31% of all reviews). 177,666 were claimed for 6,403 legitimate journals (96.69% of all reviews). The vast majority of scholars either never or only occasionally submitted reviews for predatory journals to Publons (89.96% and 7.55% of all reviewers, respectively). Smaller numbers of scholars claimed reviews predominantly or exclusively for predatory journals (0.26% and 0.35% of all reviewers, respectively). The two latter groups of scholars are of younger academic age and have fewer publications and fewer reviews than the first two groups of scholars.Developing regions feature larger shares of reviews for predatory reviews than developed regions. ConclusionThe characteristics of scholars who review for potentially predatory journals resemble those of authors who publish their work in these outlets. In order to combat potentially predatory journals, stakeholders will need to adopt a holistic approach that takes into account the entire research workflow.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
PLOS ONE
4510 papers in training set
Top 1%
39.4%
2
Wellcome Open Research
57 papers in training set
Top 0.1%
5.1%
3
PeerJ
261 papers in training set
Top 0.8%
5.1%
4
FEBS Letters
42 papers in training set
Top 0.1%
4.8%
50% of probability mass above
5
Philosophical Transactions of the Royal Society B
51 papers in training set
Top 1.0%
4.1%
6
FACETS
11 papers in training set
Top 0.1%
3.7%
7
F1000Research
79 papers in training set
Top 1.0%
2.2%
8
Scientific Reports
3102 papers in training set
Top 49%
2.2%
9
JAMA Network Open
127 papers in training set
Top 2%
2.0%
10
Royal Society Open Science
193 papers in training set
Top 2%
1.8%
11
FEBS Open Bio
29 papers in training set
Top 0.1%
1.8%
12
eneuro
389 papers in training set
Top 5%
1.8%
13
PLANTS, PEOPLE, PLANET
21 papers in training set
Top 0.3%
1.7%
14
Nature Human Behaviour
85 papers in training set
Top 2%
1.6%
15
eLife
5422 papers in training set
Top 48%
1.3%
16
The FEBS Journal
78 papers in training set
Top 0.4%
1.3%
17
Bioinformatics
1061 papers in training set
Top 8%
0.9%
18
Journal of Cellular Physiology
21 papers in training set
Top 0.5%
0.9%
19
Journal of General Internal Medicine
20 papers in training set
Top 0.8%
0.9%
20
PLOS Biology
408 papers in training set
Top 16%
0.9%
21
International Journal of Environmental Research and Public Health
124 papers in training set
Top 7%
0.8%
22
Medicine
30 papers in training set
Top 2%
0.8%
23
BMC Public Health
147 papers in training set
Top 6%
0.8%
24
Heliyon
146 papers in training set
Top 6%
0.8%
25
FASEB BioAdvances
15 papers in training set
Top 0.3%
0.8%
26
Behavior Research Methods
25 papers in training set
Top 0.3%
0.7%
27
Ecology and Evolution
232 papers in training set
Top 5%
0.5%