Open access publishing – noble intention, flawed reality
Bibliographic record
Abstract
For two decades, the international scholarly publishing community has been embroiled in a divisive debate about the best model for funding the dissemination of scientific research. Some may assume that this debate has been thoroughly resolved in favour of the Open Access (OA) model of scientific publishing. Recent commentaries reveal a less settled reality. This narrative review aims to lay out the nature of these deep divisions among the sector's stakeholders, reflects on their systemic drivers and considers the future prospects for actualising OA's intended benefits and surmounting its risks and costs. In the process, we highlight some of inequities OA presents for junior or unfunded researchers, and academics from resource-poor environments, for whom an increasing body of evidence shows clear evidence of discrimination and injustice caused by Article Processing Charges. The authors are university-appointed researchers working the UK and South Africa, trained in disciplines ranging from medicine and epidemiology to social science and digital science. We have no vested interest in any particular model of scientific publication, and no conflicts of interest to declare. We believe the issues we identify are pertinent to almost all research disciplines.
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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 | 0.166 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".