The state and evolution of Gold open access: a country and discipline level analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.051 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it