The UNESCO OER Recommendation: Some Observations From the ICDE OER Advocacy Committee
Bibliographic record
Abstract
In this article, ambassadors of the International Council for Open and Distance Education (ICDE) Open Educational Resources (OER) Advocacy Committee (OERAC) provide a snapshot of regional and global Open Educational Resources (OER) initiatives. This committee has been active since 2017 with membership renewed biannually. The ambassadors work to further OER awareness and understanding, to increase global recognition of OER, and provide policy support for the acceptance and application of OER. This overview highlights national and regional initiatives associated with the UNESCO OER recommendation and the five action areas that include: building capacity and leveraging OER; developing supporting policies; ensuring equity and effectiveness; encouraging sustainable OER model development; and, promoting and facilitating international collaboration. In addition, monitoring and evaluation of the action areas are suggested to be prioritized. This overview is not exhaustive, and much work remains to implement the OER Recommendation at scale, maximize its implementation, connect these recommendations to the United Nation’s Sustainable Development Goals (SDGs), along with the futures of education with a new social contract for education, individuals, and the planet.
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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 | 0.086 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".