G20 Countries Status on Institutional Repositories in OpenDOAR: An Exploratory Study
Bibliographic record
Abstract
The G20 (Group of Twenty) consists of 19 countries and the European Union (EU). The 19 countries that are members of the G20.This Paper aims to examine the growth and development of G20 Countries status on Institutional Repositories available in OPENDOAR. The Data was collected from the Directory of OPENDOAR. The study finds that the USA with 926 (25%) number of repositories in OPEN DOAR leads the table followed by Japan with 627 (12%), United Kingdom with 0324, Turkey with 180, Indonesia with 174, Brazil with 170, France with 160, Italy with 143, India with 106, Canada with 100, Australia with 91, Argentina with 84, China 65, Maxico with 53, Russia with 50, South Africa 50. Taken together, a total of 3643 Contributions are found in OPEN DOAR of G20 countries. A total of 1006 repositories that use Dspace software belong to Argentina 46, Australia 21, Brazil 143, Canada 33, China 37, France 10, Germany 44 India 58, Indonesia 16, Italy 75, Japan 60, Mexico 33, Russia 33, Saudi Arabia 7, South Africa 32, Turkey 150, United Kingdom 50. The study is original work intended to examine the contributions of G20 countries in OPENDOAR.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".