Blacklisting or Whitelisting? Deterring Faculty in Developing Countries from Publishing in Substandard Journals
Bibliographic record
Abstract
A thriving black-market economy of scam scholarly publishing, typically referred to as ‘predatory publishing,’ threatens the quality of scientific literature globally. The scammers publish research with minimal or no peer review and are motivated by article processing charges and not the advancement of scholarship. Authors involved in this scam are either duped or willingly taking advantage of the low rejection rates and quick publication process. Geographic analysis of the origin of predatory journal articles indicates that they predominantly come from developing countries. Consequently, most universities in developing countries operate blacklists of deceptive journals to deter faculty from submitting to predatory publishers. The present article discusses blacklisting and, conversely, whitelisting of legitimate journals as options of deterrence. Specifically, the article provides a critical evaluation of the two approaches by explaining how they work and comparing their pros and cons to inform a decision about which is the better deterrent.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.065 | 0.170 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.012 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".