The state and evolution of Gold open access: a country and discipline level analysis
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the evolution of Gold open access (OA) rates in different countries and disciplines, as well as explore the influencing factors. Design/methodology/approach In this study, employing the OA filter option of Web of Science (WoS), the authors perform a large-scale evaluation of the OA state of countries and disciplines from 1990 to 2016. Particularly, the authors consider not only the absolute number of Gold OA literature but also the ratio of them among all literature. Findings Currently, one-quarter of the WoS articles is Gold OA articles. Brazil is the most active country in OA publishing, while Russia, India and China have the lowest OA ratios. The OA percentage of Brazil has been decreasing dramatically in recent years, while the OA percentages of China, UK and the Netherlands have been increasing. There also exist huge differences of OA percentages across different subject categories. The percentages of OA articles in biology, life, and health-related areas are high, while those in physics and chemistry-related subject categories are very low. Originality/value With the availability of large-scale data from WoS, this study conducts a comprehensive evaluation of the Gold OA state of major countries for the first time. The variation of OA percentages is considered in light of the research profiles. OA policies in different countries and funding organizations also have an influence on the OA development.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.046 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".