Techniques predatory journals use to solicit manuscripts: a contrast between Africa (lower- income countries) and Canada (high income country)
Bibliographic record
Abstract
Abstract Background Predatory journals charge authors for publication without quality peer review or editorial services. They target researchers not only in high-(HIC) but also in lower income countries (LIC). The purpose of this study was to investigate characteristics of predatory journals and their recruitment techniques aimed at researchers in LIC and HIC. Methods Four clinician researchers, two in lower income countries (Kenya and Uganda) and two in a high-income country (Canada), identified and collected unsolicited emails from suspected predatory journals over a six-month period in 2019; 50 randomly selected emails from researchers in Canada and 50 from African researchers. These were assessed for similarities and differences using a set of criteria derived from the literature on predatory publishing. Findings: Features common to both the LIC and HIC groups included requesting email not website manuscript submission and claiming a very rapid processing timeline. In comparison to the HIC group, emails from the LIC group were significantly more likely to have impersonal greetings, poor integrity, request an urgent reply, and request an email back to unsubscribe from further emails. The websites of journals targeting LIC researchers were significantly more likely to use language that overly flattered authors and lacked a description of the peer review process. HIC target emails were more likely to be from journals out of scope of the authors’ work. Interpretation: We identified similarities and differences between LIC and HIC predatory journal submission request emails and their websites. Being aware of these different approaches may help authors better avoid predatory publishing in future.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | MetaresearchScholarly communicationResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.015 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".