Evidence-Based Policy Development: National Adaptation Strategy and Plan of Action on Climate Change for Nigeria (NASPA-CCN)
Bibliographic record
Abstract
Abstract Evidence-based policies are recommended for the enhanced chances of efficacy in achieving policy goals. Achieving this in the policy development process may however require approaches that are not as simple especially in less developed countries, where the research-policy linkage is not commonly the case. This chapter provides a guide to a practical approach that could assist policy makers in similar societies based on the steps adopted in the development of the National Adaptation Strategy and Plan of Action on Climate Change (NASPA-CCN) for Nigeria. The NASPA–CCN has been acknowledged as among the models of climate change policy development that other countries could aim for. It is therefore positioned to offer lessons on policy development in a less developed country environment. The focus in this chapter however is not so much on the subject of climate change but the practical experiences and lessons learnt from the process involved in developing the NASPA-CCN providing lessons learned to mainstream climate change research evidence into policy.
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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 | 0.040 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".