“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.
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 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 | 0.064 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.034 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".