Harnessing the sun – addressing sociotechnical barriers to off-grid solar power deployment in Mozambique
Bibliographic record
Abstract
Mozambique has substantial solar power potential (23,000GWe) yet only 83MWe of installed capacity (representing 2% of the total 3623MWe generation capacity). Meanwhile, 44% of the population has electricity access, making Mozambique one of the least-electrified countries. Efforts to scale-up off-grid solar photovoltaics and improve rural electrification face key sociotechnical challenges. Using interview data from 33 national stakeholders, we identify the key policy, inter-agency coordination, socio-cultural development, and institution-driven actions needed to overcome these challenges. We introduce the concept of a ‘social multiplier effect’ to explain how small-scale electricity access improvements increase public demand for grid-based electrification, demonstrating how this can drive socioeconomic benefits to rural and peri-urban areas. We call for coordinated actions from policy and market actors, advocating for policy coherence and increased private sector involvement to boost investment, regulation and innovation in off-grid solar technologies, ultimately achieving universal electricity access and improving social development outcomes in Mozambique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".