“I’d Like to Think I’d Be Able to Spot One”: How Journalists Navigate Predatory Journals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Predatory journals—or journals that prioritize profits over editorial and publication best practices—are becoming more common, raising concerns about the integrity of the scholarly record. Such journals also pose a threat for the integrity of science journalism, as journalists may unwillingly report on low-quality or even highly flawed studies published in these venues. This study sheds light on how journalists navigate this challenging publishing landscape through a qualitative analysis of interviews with 23 health, science, and environmental journalists from Europe and North America about their perceptions of predatory journals and strategies for ensuring the journals they report on are trustworthy. We find that journalists have relatively limited awareness and/or concern about predatory journals. Much of this attitude is due to confidence in their established practices for avoiding problematic research, which largely centre on perceptions of journal prestige, reputation, and familiarity, as well as writing quality and professionalism. Most express limited awareness of how their trust heuristics may discourage them from reporting on smaller, newer, and open access journals, especially those based in the Global South. We discuss implications for the accuracy and diversity of the science news that reaches the public.
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.
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 | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Scholarly communicationResearch integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it