Gold Open Access Publishing in Mega-Journals: Developing Countries Pay the Price of Western Premium Academic Output
Bibliographic record
Abstract
Open access publishing (OAP) makes research output freely available, and several national governments have now made OAP mandatory for all publicly funded research. Gold OAP is a common form of OAP where the author pays an article processing charge (APC) to make the article freely available to readers. However, gold OAP is a cause for concern because it drives a redistribution of valuable research money to support open access papers in ‘mega-journals’ with more permissive acceptance criteria. We present a data-driven evaluation of the financial ramifications of gold OAP and provide evidence that gold OAP in mega-journals is biased toward Western industrialized countries. From 2011 to 2015, the period of our data collection, countries with developing economies had a disproportionately greater share of articles published in the lower-tier mega-journals and thus paid article APCs that cross-subsidize publications in the top-tier journals of the same publisher. Conversely, scientists from Western developed countries had a disproportionately greater share of articles published in those same top-tier journals. The global inequity of the cross-subsidizing APC model was demonstrated across five different mega-journals, showing that the issue is a common problem. We need to develop stringent and fair criteria that address the global financial implications of OAP, as publication fees should reflect the real cost of publishing and be transparent for authors.
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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 | 0.021 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".