Bibliographic record
Abstract
The open-source software movement is gaining increasing momentum in China. Of the limited numbers of open-source software in China, Red Flag Linux stands out most strikingly, commanding 30 percent share of Chinese software market. Unlike the spontaneity of open-source movement in North America, open-source software development in China, such as Red Flag Linux, is an orchestrated activity wherein different levels of government play a vital role in sponsoring, incubating, and using open-source software, most conspicuously, Red Flag Linux. While there are no reports on open-source course management system in China, there are reports on the study and use of Western open-source course management systems for instruction and learning in Chinese higher education institutions. This paper discusses the current status of open-source software in China, including open-source course management software and associated tools and resources. Importantly, it describes the development model of Red Flag Linux, the most successful open-source software initiative in China. In addition, it explores the possibility of Chinese higher education institutions joining efforts to develop China’s own open-source course management system using the open-source development model established in North America. A timeline of major open-source projects of significance underway in China is provided. The paper concludes with a discussion of the potential for applying the open-source software development model to open and distance education in China.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".