An African Rural Internet Network, and its interactions with Academics
Bibliographic record
Abstract
Creating ICT access in the rural areas of Africa is a complex challenge. In these areas, telecommunications infrastructure is provided at a very high cost that may not be justified by the resulting use and effects of the telecommunications network. Affordable access to ICTs in rural areas can be frustrated at the supply as well as the demand end of the service-provision chain. To supply ICTs and related services in rural areas, the main challenge is the high level of capital and operating expenditures incurred by service providers. On the demand side, rural adoption of ICTs in developing countries is curtailed by low availability of complementary public services, such as electricity and education, and by the relative scarcity of locally relevant content. This paper highlight the various aspects of the challenges of setting up a First Mile project in rural Zambia. It takes a rather unique angle in that it introduces the perspectives on the value of academic interventions in a manner that not only addresses some of the complex issues but also accommodates cultural adjustment. The paper introduces the rural internet project in Macha, rural Zambia and demonstrates the impact of such an initiative on setting up not only connectivity in a rural community but also dealing with the challenges that come with this.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".