“I’d like to think I’d be able to spot one if I saw one”: How science journalists navigate predatory journals
Bibliographic record
Abstract
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 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.
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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 | MetaresearchScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | Research integrityScholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".