International Development Partnerships and Diffusion of Renewable Energy Powered Lighting Technologies in Off-Grid Communities in Developing Countries
Bibliographic record
Abstract
Worldwide, 1.4 billion people virtually live in darkness after sunset. New lighting technologies, such as light emitting diodes (LED), powered by renewable energy allow non-electrified communities to access for the first time in their lives clean, durable, affordable, and higher quality lighting service. The international NGOs play an important intermediary role in diffusion of these technologies to off-grid communities and commonly operate via development partnerships. With the goal of providing insights into the nature of these partnerships and factors that influence their effectiveness, the exploratory case study was conducted which examined and compared development partnerships promoting renewable energy powered lighting technologies in off-grid indigenous communities in Talamanca, Costa Rica. The study acknowledged the catalytic role of the international NGOs and emphasized the centrality of locally embedded organizations and their capacities in successful implementation of development interventions. A set of factors was identified that contribute to the effectiveness of the development partnership in renewable energy in off-grid 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".