Are most FT50 journals truly “suspected predatory”, or has an AI-driven “predatory” journal detector erred in its classification?
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
A paper published in Scientific Reports introduced a tool, the Academic Journal Predatory Checking System (AJPCS), that claims to be able to recognize scholarly (“normal”) from “suspected predatory” journals (SPJs). On 8-11 March 2023, that tool was used to verify the classification of 50 top-ranked journals according to the Financial Times (FT50), finding that 88% of them were classed as SPJs. Classifying them as SPJs is a hefty negative reputational label to assign to journals whose publishing practices and scholarly characteristics have likely been fairly carefully screened prior to their indexing and ranking.
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.
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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.066 | 0.109 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.008 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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