The Rise of Platinum Open Access Journals with Both Impact Factors and Zero Article Processing Charges
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
It appears that open access (OA) academic publishing is better for science because it provides frictionless access to make significant advancements in knowledge. OA also benefits individual researchers by providing the widest possible audience and concomitant increased citation rates. OA publishing rates are growing fast as increasing numbers of funders demand it and is currently dominated by gold OA (authors pay article processing charges (APCs)). Academics with limited financial resources perceive they must choose between publishing behind pay walls or using research funds for OA publishing. Worse, many new OA journals with low APCs did not have impact factors, which reduces OA selection for tenure track professors. Such unpleasant choices may be dissolving. This article provides analysis with a free and open source python script to collate all journals with impact factors with the now more than 12,000 OA journals that are truly platinum OA (neither the author nor the readers pay for the peer-reviewed work). The results found platinum OA is growing faster than both academic publishing and OA publishing. There are now over 350 platinum OA journals with impact factors over a wide variety of academic disciplines, giving most academics options for OA with no APCs.
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 | 0.015 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.073 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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