Involvement of China and its close partners in the international open access movement: Quantitative analysis and benchmarking approach
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.025 | 0.053 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| 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