MétaCan
Menu
Back to cohort
Record W3137692099 · doi:10.1057/s41599-021-00754-5

“These are the realities”: insights from facilitating researcher-policymaker engagement in Nigeria’s household energy sector

2021· article· en· W3137692099 on OpenAlexfundno aff
Temilade Sesan, W.O. Siyanbola

Bibliographic record

VenueHumanities and Social Sciences Communications · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersInternational Science CouncilInternational Development Research Centre
KeywordsContext (archaeology)Knowledge productionCitizen journalismGovernment (linguistics)Public relationsProduction (economics)Process (computing)Political scienceSpace (punctuation)PopulationEnergy (signal processing)Economic growthSociologyBusinessPublic administrationEconomicsKnowledge managementGeography

Abstract

fetched live from OpenAlex

Abstract Energy has been a key focus of government policy in Nigeria for decades, yet little improvement has been seen in rates of access among the population. Our paper assesses the inputs to policymaking in this context and interrogates the role of scientific evidence and knowledge co-production in the process. Through key informant interviews and participatory workshops with stakeholders, we addressed the practical question of how to strengthen the contribution of evidence to national energy policymaking processes. Two windows of opportunity were identified for this: the critical stage of problem definition; and the time lag between policy adoption and implementation. By engaging proactively with policymakers on these fronts, academic researchers working in knowledge co-production arrangements can make quick inroads into a policy space that has largely excluded them to date. This case is instructive for academics and knowledge brokers in similar contexts where a diminished status for scientific evidence might make more ambitious exchanges with policy difficult, to begin with.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.252
GPT teacher head0.315
Teacher spread0.063 · 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.

Study designQualitative
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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueHumanities and Social Sciences CommunicationsSame topicEnergy and Environment ImpactsFrench-language works237,207