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Record W3158306753 · doi:10.24908/iqurcp.7176

Access to Lighting in Northern Ghana: Is Solar Power the Answer?

2017· article· en· W3158306753 on OpenAlexvenueno aff
Amy Buitenhuis, Lindsay Wiginton

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsRural areaBusinessElectricityRural electrificationEconomic growthGeographyEnvironmental planningEnvironmental resource managementEngineeringPolitical scienceElectrificationEconomics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.106
GPT teacher head0.375
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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