The logic of national policies and strategies for open educational resources
Bibliographic record
Abstract
In its first decade (2001-2010) the OER movement has been carried by numerous relevant and successful projects around the globe. These were sometimes large-scale but more often not, and they were primarily initiated by innovating educational institutions and explorative individual experts. What has remained, however, is the quest for a sustainable perspective, in spite of the many attempts in the OER community for clear-cut solutions to the problem of sustainability. This is a major barrier for mainstreaming the OER approach in national educational systems. At the end of the first decade, and more so at the beginning of the second decade (2011-2020), we are witnessing in a few countries emerging efforts to develop and establish a national OER approach. That is required in order to break down the barrier for mainstreaming OER. Making the OER approach sustainable cannot be left to the educational institutions only, but should be facilitated in a national setting.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".