MétaCan
Menu
Back to cohort
Record W4410995438 · doi:10.1080/2157930x.2025.2514919

Harnessing the sun – addressing sociotechnical barriers to off-grid solar power deployment in Mozambique

2025· article· en· W4410995438 on OpenAlexaff
Daniela Salite, Matthew Cotton, Joshua Kirshner

Bibliographic record

VenueInnovation and Development · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersForeign, Commonwealth and Development OfficeGovernment of the United Kingdom
KeywordsSociotechnical systemSoftware deploymentGridSolar powerPower gridSmart gridPower (physics)Computer scienceBusinessEnvironmental scienceEngineeringElectrical engineeringGeographyPhysicsKnowledge management

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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 teacher head, 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInnovation and DevelopmentSame topicEnergy and Environment ImpactsFrench-language works237,207