Bibliographic record
Abstract
The poorer countries of Africa, especially the most disadvantaged communities are often unable to benefit from the advances in information and communica1tion technologies (ICTs) although several African countries are rapidly mastering these technologies. The reasons include isolation, lack of means, insufficient infrastructure and cultural factors. This is particularly the case in the remote rural areas of Africa where the majority of the people are living. On the other hand they have particularly critical needs for support for development information, education and training, employment opportunities and public services in general as well as community action to improve the situation. African rural economies which depend on agriculture, fishing and small scale industry and business activities, need to be diversified so as to preserve their indigenous character, attract new business and have access to external markets, to decision makers and to information providers. Reports on the pilot projects to establish Multipurpose Community Telecentres (MCTs) in selected African countries by Unesco in collaboration with the International Development Research Centre (IDRC), Canada, the World Bank, UNDP and other agencies. The infrastructure, budget, methodology and strategy adopted to ensure self-sustainability, and other features of the projects including start-up operations and training of personnel are discussed. The lessons learnt from the projects are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| 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".