Sustaining capacity for supplying evidence for agricultural & rural development policies
Bibliographic record
Abstract
"The Federal Government of Nigeria has demonstrated a strong commitment to achieving the Millennium Development Goals (MDGs) and the NEEDS Targets. Recognizing the importance of agriculture, as well as the challenges faced by the sector, in providing evidence for policymaking, the Federal Ministry of Agriculture and Water Resources (FMAWR) established the Agricultural Policy Support Facility (APSF), with the International Food Policy Research Institution (IFPRI) as its implementing partner and the Canadian International Development Agency (CIDA) providing financial support. APSF aims to strengthen the capacity of FMAWR to design and implement agricultural policies by addressing the fundamental knowledge, capacity, and communication problems faced by the Government of Nigeria. In order to support evidence-based policymaking, it is essential to identify the current capacity within Nigeria for providing evidence for policymaking and for creating this capacity for future generations. In collaboration with the University of Ibadan, University of Agriculture-Abeokuta, FMAWR, and IFPRI, a consultation workshop on “Sustaining Capacity for Supplying Evidence for Agricultural Rural Development Policies and Strategies was held at the University of Ibadan on April 28, 2008. The objectives of this consultative workshop were to identify: specific capacity challenges confronting university professors who are teaching the next generation of policymakers, policy analysts, researchers and university professors; curriculum gaps within the university programs for building capacity for designing and implementing pro-poor, gender sensitive, and environmentally sustainable agricultural and rural development policies and strategies; employment opportunities for the university graduates in agricultural economics and extension and the required skills; existing capacity for undertaking agriculture and rural development policy research; and current methods used by researchers to convey their results to decisionmakers and other stakeholders." --from text
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 | 0.158 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.009 | 0.011 |
| 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".