Bibliographic record
Abstract
In the race towards achieving the Education 2030 agenda, open educational resources (OER) act as a key enabler for sustainable development goal 4 (SDG4). Leading to the 2014 Regional Focal Points Meeting, Commonwealth of Learning’s (COL) Focal Point for Tonga had identified top priorities for the country where COL can further support the national agenda till 2021. Based on these needs, the Strategic OER Implementation Project in Tonga was initiated by COL in response to a request by the Ministry of Education and Training (MET) of Tonga. The project aims to assist MET in (a) developing a framework for fully utilizing the new fiber optic network infrastructure to deliver online learning to Tongans distributed in the 45 islands; and (b) improve the chances of sustainable livelihoods for Tongan youth by training them in life skills tailored to higher education and employment opportunities in Australia and New Zealand. This paper details the use of the horizontal framework for OER mainstreaming and the OER mainstreaming checklist within this project. The novelty of this project is its approach to mainstreaming OER at an institution in a systemic manner. The contribution this paper makes is to provide a proven plan for sustainable OER mainstreaming in a development 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".