A Collaborative Approach to OER Policy and Guidelines Development in the Commonwealth: The Case of Botswana, Cameroon, and Sri Lanka
Bibliographic record
Abstract
Access to relevant learning resources is an important aspect in ensuring inclusive and equitable quality education and lifelong learning opportunities for all as outlined in the sustainable development goal 4 (SDG4). The Commonwealth of Learning (COL) has identified the development of open educational resources (OER) as a potential answer to these challenges. A total of 29 provincial/regional OER policies and guidelines were developed in Sri Lanka, Botswana, and Cameroon closely involving 608 provincial/regional policymakers from the general education system. The innovation of this project lies in the collaborative approach adopted for OER policy/guideline development where a maximum number of policymakers at the provincial/regional level have been included in the policy development process. Key applications of the approach are mass-sensitization of policymakers, identification of champions in each province or region to drive the OER agenda forward, and the development of policies/guidelines tailored to the specific needs of a particular jurisdiction. The paper will also highlight the success factors, challenges, and the follow-up activities of the project.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".