Open, Distance, and Digital Non-formal Education in Developing Countries
Bibliographic record
Abstract
Abstract Non-formal education contributes significantly to improve the literacy and livelihoods of individuals. Its significance becomes much more in developing countries where 70% of the world population lives. However, population densities, geographical diversities, and varied socioeconomic conditions in many developing countries make it difficult to offer need-based non-formal education (NFE) to all. Fortunately, open, distance, and digital education (ODDE) has emerged as a viable approach to offer quality non-formal education programs at a minimal cost. Research reveals that proper and effective use of ODDE to offer NFE changes the lives of many citizens in developing countries and may help these countries achieve the Sustainable Development Goals. This chapter presents in its first section an overview of the use of ODDE for supporting NFE initiatives in the developing world and identifies issues and challenges faced. The next section of the chapter outlines theoretical insights and findings of valued publications regarding the use of ODDE for offering NFE. The final section provides the strategies for making the best and optimum use of ODDE to make NFE accessible to all eligible and willing ones in developing countries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".