“These are the realities”: insights from facilitating researcher-policymaker engagement in Nigeria’s household energy sector
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".