Questioning the efficacy of ‘gold’ : open access to published articles
Bibliographic record
Abstract
Aim To question the efficacy of ‘gold’ open access to published articles. Background Open access is unrestricted access to academic, theoretical and research literature that is scholarly and peer-reviewed. Two models of open access exist: ‘gold’ and ‘green’. Gold open access provides everyone with access to articles during all stages of publication, with processing charges paid by the author(s). Green open access involves placing an already published article into a repository to provide unrestricted access, with processing charges incurred by the publisher. Data sources This is a discussion paper. Review methods An exploration of the relative benefits and drawbacks of the ‘gold’ and ‘green’ open access systems. Discussion Green open access is a more economic and efficient means of granting open access to scholarly literature but a large number of researchers select gold open access journals as their first choices for manuscript submissions. This paper questions the efficacy of gold open access models and presents an examination of green open access models to encourage nurse researchers to consider this approach. Conclusion In the current academic environment, with increased pressures to publish and low funding success rates, it is difficult to understand why gold open access still exists. Green open access enhances the visibility of an academic’s work, as increased downloads of articles tend to lead to increased citations. Implications for research/practice Green open access is the cheaper option, as well as the most beneficial choice, for universities that want to provide unrestricted access to all literature at minimal risk. Keywords Open access, self-archiving, publishing, repository, scholarly literature, dissemination
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.
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 | 0.522 | 0.788 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.008 | 0.124 |
| Scholarly communication | 0.034 | 0.071 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".