A Comparative Study of National Infrastructures for Digital (Open) Educational Resources in Higher Education
Bibliographic record
Abstract
This paper reports on the first stage of an international comparative study for the project “Digital educational architectures: Open learning resources in distributed learning infrastructures–EduArc”, funded by the German Federal Ministry of Education and Research. This study reviews the situation of digital educational resources (or (O)ER) framed within the digital transformation of ten different Higher Education (HE) systems (Australia, Canada, China, Germany, Japan, South Africa, South Korea, Spain, Turkey and the United States). Following a comparative case study approach, we investigated issues related to the existence of policies, quality assurance mechanisms and measures for the promotion of change in supporting infrastructure development for (O)ER at the national level in HE in the different countries. The results of this mainly documentary research highlight differences and similarities, which are largely due to variations in these countries’ political structure organisation. The discussion and conclusion point at the importance of understanding each country’s context and culture, in order to understand the differences between them, as well as the challenges they face.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".