Predatory journals in dermatology: a bibliometric review
Bibliographic record
Abstract
Purpose By distorting the peer review process, predatory journals lure researchers and collect article processing charges (APCs) to earn income, thereby threatening clinical decisions. This study aims to identifying the characteristics of predatory publishing in the dermatology literature. Design/methodology/approach The authors used Kscien's list to detect dermatology-related predatory journals. Bibliometric parameters were analyzed at the level of journals, publishers, documents and authors. Findings Sixty-one potential predatory dermatology publishers published 4,164 articles in 57 journals from 2000 to 2020, with most publishers claiming to be located in the United States. Most journals were 1–5 years old. Six journals were indexed in PubMed, two in Scopus and 43 in Google Scholar (GS). The average APC was 1,049 USD. Skin, patient, cutaneous, psoriasis, dermatitis and acne were the most frequently used keywords in the article's title. A total of 1,146 articles in GS received 4,725 citations. More than half of the journals had <10 citations. Also, 318 articles in Web of Science were contaminated by the most cited articles and 4.49% of the articles had reported their funding source. The average number of authors per article was 3.7. India, the United States and Japan had the most articles from 119 involved countries. Asia, Europe and North America had the most contributed authors; 5.2% of articles were written through international collaboration. A majority of authors were from high- and low-middle-income countries. Women contributed 43.57% and 39.66% as the first and corresponding authors, respectively. Research limitations/implications The study had limitations, including heavy reliance on Kscien's list, potential for human error in manual data extraction and nonseparation of types of articles. Journals that only published dermatology articles were reviewed, so those occasionally publishing dermatology articles were missed. Predatory journals covering multiple subjects (Petrisor, 2016) may have resulted in overlooking some dermatology papers. This study did not claim to have covered all articles in predatory dermatology journals (PDJs) but evaluated many of them. The authors accept the claim that Kscien's list may have made a mistake in including journals. Originality/value The wide dispersion of authors involved in PDJs highlights the need to increase awareness among these authors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.162 | 0.175 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".