Fighting Predatory Journals: A Strategic Solution for the Quality and Sustainability of Scientific Publications in Indonesia
Bibliographic record
Abstract
The phenomenon of predatory journals seriously threatens the academic ecosystem, especially in Indonesia, where the pressure to "publish or perish" and low academic literacy exacerbate the situation. Predatory journals offer a fast publication process without adequate peer review, thereby lowering the quality of research, hurting the reputation of academics and institutions, and spreading invalid information. Although many global studies have addressed this issue, there are research gaps related to the Indonesian context, especially strategic solutions that consider regulation, literacy, and management of scientific journals. This research aims to identify the impact of predatory journals, outline the challenges for academics and journal managers, and offer strategic solutions based on Agency and Knowledge-Based Theory. The research method used is descriptive-analytical with a qualitative approach based on secondary data from scientific literature. The results show that the main challenges are low academic literacy, pressure to publish, limited access to reputable journals, and financial exploitation. Strategic solutions include education through programs such as “Think, Check, Submit.” Strengthening regulations, using technology to detect predatory journals, and national and international collaboration. The role of journal managers is vital in maintaining the quality of publications through editorial transparency, certification, technological innovation, and the development of the reviewer community. The implications of this study emphasize the importance of collaboration and academic literacy in creating a healthy academic ecosystem. The novelty research lies in integrating Agency and Knowledge-Based theories in the context of predatory journals in Indonesia, offering relevant and applicable strategic solutions to support the sustainability of high-quality scientific publications.
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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 | Not applicable | low |
| gpt | Scholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".