Injustices in rural electrification: Exploring equity concerns in privately owned minigrids in Tanzania
Bibliographic record
Abstract
Access to electricity is vital for many basic needs, but it is often unaffordable. Since 2008, there has been an increase in private companies providing electricity access in rural areas through solar minigrids in Tanzania. This paper focuses on the different tariffs used in projects in Tanzania and how they distribute costs. Data were collected over eight months of fieldwork in 2019/2020 from six rural communities through interviews, focus groups, surveys, and directly from minigrid companies. I have found that by private companies treating electricity as an economic good communities experience energy injustices. Under many tariffs poorer households pay more per unit than those with higher incomes. Poorer households are less likely to be able to connect and under some tariffs self-disconnect from their electricity service. Barriers such as lack of participation in project development, high tariffs, and high connection fees limit the benefits of rural electrification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".