Involvement of China and its close partners in the international open access movement: Quantitative analysis and benchmarking approach
Bibliographic record
Abstract
A quantitative assessment of China and its close partners’ involvement in the Open Access movement was conducted using a specially developed index ranging from zero to one. The index is based on three groups of indicators with different weights: Open Access repositories and journals (highest weight), Open Access policies (average weight), and signatories to Open Access initiatives (lowest weight). China's closest partners include 10 ASEAN countries, Russia, and 5 post-Soviet Central Asian countries. The results identify three groups of countries. Indonesia leads with a significant gap ahead of Russia and China, whose index values range between 0.3 and 0.5. The third group of 14 countries trails far behind with index values between 0.0 and 0.08. A correlation-regression analysis was performed to rank the countries according to the index under study, which revealed the stability of this ranking in its upper layer. An explanation for this phenomenon can be found in the Matthew effect and Cumulative Advantage Distribution. Regular index calculations combined with benchmarking methodology are suggested to link target index values to Open Access best practices. The conclusion will propose measures to increase the participation of less developed countries in ASEAN and Central Asia in the OA movement.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".