Development of eLearning in the Commonwealth Countries
Bibliographic record
Abstract
The Commonwealth is home to 2.4 billion people (almost one-third of the world population). The countries of Commonwealth, 54 in number, are spread across Africa, Asia, the Americas, Europe, and the Pacific. The Commonwealth countries are amongst the world's biggest and smallest states in terms of population and size, with about 32 of them having less than 1.5 million people. Over the years, the Commonwealth countries have emphasized using distance education and technologies to improve access to quality learning opportunities. In this pursuit, online learning or eLearning has been adapted in many Commonwealth countries, although in varying degrees. This chapter provides an overview of eLearning in Commonwealth countries by looking into the developments that emerged as various policies, projects, and practices in the four regions of the Commonwealth (i.e., Commonwealth Africa, Commonwealth Asia, Commonwealth Caribbean, and Commonwealth Pacific).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".