The state and evolution of Gold Open Access: A country 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
The newly released refine option of Open Access on the Web of Science platform makes it possible to analyze the article-level OA content across the whole Web of Science database, including more than sixty million documents. In this study, employing the OA filter option of Web of Science, we perform a large-scale evaluation of the OA state of countries from 1990 to 2016. Particularly, for each country, we consider not only the absolute number of Gold OA literature but also the ratio of them among all literature. We compare the rates and evolutions of OA across countries. Our results show that the number of OA articles have increased quickly in the last decades. Currently, one quarter of the Web of Science articles are Gold OA articles; In contrast, in 1990, the percentage of OA articles is less than 8%. Brazil is found to be the most active country in OA publishing. In contrast, Russia, India and China have the lowest OA ratios. In addition, the temporal trend analysis shows that the OA percentage of Brazil has been decreasing dramatically in recent years, while the OA percentages of China, UK and Netherlands have been increasing.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometricsOpen science Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsOpen science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.004 |
| 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