Bibliographic record
Abstract
The advent of open access publishing necessitates evaluating the quality of a plethora of new journals. The problem of ensuring quality is inherent in the benefits and goals of open access publishing, which attempts to establish a system for reporting research findings that is inclusive and expeditious. However, inclusivity and speed may run counter to the goals of quality and reliability, and the pressure for researchers to publish creates incentives to participate in a fraudulent system. This paper presents an alternative approach to evaluating the legitimacy of open access publications. Those concerned about the quality of open access publishing have attempted to evaluate journals based on criteria that refer to externally available information. The approach used here provides additional, internal information about participation in journals' review processes. This additional information, namely, documentation of the process from submission through review to acceptance, is crucial for evaluating potentially fraudulent open access journals that might appear legitimate based on publicly available information.
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.
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 | 0.181 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.031 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".