Correlations in APC, IF, and Publication Output from Authors in Lower Income Countries
Bibliographic record
Abstract
Open Access (OA) scholarly journals typically follow a fee-based publishing model where authors pay article processing charges (APCs) to publish their content, allowing readers to access it free of charge without any restrictions. This fee-based structure places the financial burden on authors, as opposed to those who choose to publish in subscription-based journals, where there is generally no cost to the authors. To reduce or remove financial barriers, publishers may provide APC waivers or discounts to authors based in low- or middle-income countries. We explored the relationship between impact factor and APC with publication output from low- and middle-income countries in a wide range of journals. We compare the geographic distribution of published content in a subset of OA journals in physical sciences, biological sciences, and social sciences. We chose journals that have different APC amounts, as these can vary widely (e.g., megajournals in the physical sciences with APCs as low as 675 USD [IOP SciNotes] to as high as 6290 USD [Nature Communications]. Correlated trends in higher publication output from authors in lower-income countries in journals with lower APCs, and lower publication output in higher APC journals could indicate that APC amounts are a factor for authors when choosing which journals to target for publication. This analysis will identify concerns around equity in OA publishing and discuss whether the current mechanisms are sufficient to support authors from lower-income countries in publishing their research in journals of their choice with desired high impact.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".