A Comparative Study of National Infrastructures for Digital (Open) Educational Resources in Higher Education
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
This paper reports on the first stage of an international comparative study for the project “<em>Digital educational architectures: Open learning resources in distributed learning infrastructures–EduArc</em>”, 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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