What Do They Say? Authors of Articles in Predatory Journalism and Mass Communication Journals Speak
Bibliographic record
Abstract
Journalism and mass communication (J&MC) research examines crucial issues in democratic and undemocratic societies, such as freedom of expression, misinformation and disinformation, government regulation of communications, defamation and invasion of privacy, media technologies and economics, and journalists’ professional practices. Unethical scholarship practices may weaken societal and public policy goals of fair, independent, and accurate reporting and transparent governance. This case study analyses how one predatory J&MC journal recruits authors to submit their work and why some scholars succumb to such invitations. This research contributes to both the growing scholarship about predatory publishing practices and to further understanding of how such journals deceptively exploit authors willing to pay for publication without the traditional peer review and editing. This study uses probability sampling of authors who published 504 articles in the journal between 2011 and 2021 to seek their participation in a survey and interviews. Most authors are from developing countries, but others are from the developed world, including faculty at top-tier research institutions. Surprisingly, some published in this journal despite knowing its predatory nature. In such instances, they might benefit from a lack of policies at their universities discouraging publication in predatory journals and may receive benefits from those institutions. Some authors regretted publishing in the journal, especially if they were unaware of its predatory character, because it deprived them of an opportunity to disseminate that research in legitimate academic venues. There are significant societal and political implications as well.
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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 | 0.012 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".