Access to Lighting in Northern Ghana: Is Solar Power the Answer?
Bibliographic record
Abstract
Solar lighting technology is seen as an exciting new opportunity for developing communities to have off‐grid access to lighting. It is promoted as a viable form of appropriate technology, and there are many projects which are attempting to implement this technology in rural communities across the world, including Ghana. Compared to its counterparts, Ghana is a rapidly developing African country; however, there remains much polarization between the northern and southern regions. The northern region is generally less developed, and one challenge faced is the lack of access to electricity and lighting. A project has been initiated by an international institution to develop the market chains of retail and distribution for individual solar‐powered lights in this region. The proposed benefits of the project are better studying conditions for students, more opportunity for conducting economic activity after dark, mitigation of health risks and improved community gatherings. However, solar panels are expensive and not commonly used in these types of communities; it is important to carefully examine the feasibility of the project for the average family in northern Ghana. The authors will present socio‐economic data gathered through 59 household interviews in three communities in northern Ghana. This data includes household expenditure on current lighting sources such as kerosene, flashlight batteries, and electricity; as well as current uses of lighting. Based on the income of families in these communities, as well as their specific lighting needs, the authors hypothesize that the project proposed is not viable for the members of these rural communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".