Predatory Journals in Journalism and Mass Communication: A Case Study of Deceptions
Bibliographic record
Abstract
Predatory publishing is an increasingly difficult challenge to ignore because it threatens the integrity of research literature and scholarship. Still, this scholarly area is largely overlooked in journalism and media communications (J&MC) literature. This case study examines two J&MC journals from companies listed as possibly predatory by analyzing the experiences of scholars purportedly affiliated with them. Using a survey and interviews, the analysis suggests that these journals used deceptive and unethical tactics to recruit scholars as ostensible editorial board members and reviewers. Some scholars were listed without their consent or knowledge, and others asked unsuccessfully to be removed from the journals’ posted list of editorial board members and/or reviewers. However, some say they find their affiliation rewarding intellectually, for their careers, and for the discipline. The findings have practical implications for J&MC scholarship, especially for developing country academics with insufficient English-language proficiency and who face publish-or-perish pressures from their universities and government higher education ministries.
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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 | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Research integrityScholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| 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, 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".