Literacy at a distance in multilingual contexts: Issues and challenges
Bibliographic record
Abstract
Literacy is perhaps the most fundamental skill required for effective participation in education (formal and non-formal) for national development. At the same time, the choice of language for literacy is a complex issue in multilingual societies like Nigeria. This paper examines the issues involved, namely language policy, language and teacher development, and the role of distance education and information and communication technologies (ICTs), in making literacy accessible in as many languages as possible. Two distance learning literacy projects are presented as case studies and the lessons learned are discussed. The findings of this study suggest that although there is evidence of growing accessibility to ICTs like mobile phones, their use and success to increase access to literacy in the users’ languages are yet to be attained and maximised. The implication of the lessons learned should be relevant to other multilingual nations that seek the goal of increasing access to learning and promoting development so as to harvest economic benefits.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".