Violations of Standard Practices by Predatory Economics Journals
Bibliographic record
Abstract
This study examines factors associated with journals’ violations of scholarly ethics, referred to as predatory practices. The investigation uses a sample of economics journals listed in Cabells’ Predatory Reports with data collected from this report and the journals’ websites. Journals in this sample (average age 6.6 years) committed, on average, 7.1 predatory practices (1.9 minor, 3.3 moderate, and 1.9 severe). Notably, 90.5% of journals had a website but only 53.4% made articles accessible. India (27%), U.S. and Canada (22.3%), Nigeria (16%), and China (8.1%) were the leading locations of predatory journals. By applying Poisson regression, we examine whether web presence, accessibility of articles, journal’s age, and journal’s region help explain the number and types of predatory practices. All these factors are statistically associated with the number of minor predatory practices followed by these journals. Further, a journal’s age and region relate to the number of both moderate and severe predatory practices, unambiguously signaling deceptive and unethical publishing practices. Economics journals from India (China) have more (less) predatory practices than other regions. The results suggest that as journals age, they tend to move across types of predatory practices, which may make journals appear less predatory.
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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 | 0.009 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".