Workshop on implementing the Agricultural Policy Support Facility (APSF)
Bibliographic record
Abstract
The Agricultural Policy Support Facility (APSF) is an initiative to strengthen pro-poor, gender-sensitive, and environmentally sustainable evidence-based policymaking in Nigeria in the areas of rural and agricultural development. The program is facilitated by the Nigeria Strategy Support Program of the International Food Policy Research Institute (IFPRI) in collaboration with the Federal Ministry of Agriculture and Water Resources (FMAWR) and funded by the Canadian International Development Agency. APSF was launched in August 2007, and on September 20, 2007 a workshop was organized that brought together stakeholders to discuss the emerging issues in agriculture policy and the implementation of the APSF. The objectives of this stakeholders workshop were: to discuss the emerging issues for agricultural policy in Nigeria and how APSF can support these issues; to share information on the APSF Program and its initial activities; to receive feedback on these initial activities; and to initiate discussions on year-2 activities. This workshop was held at the Chelsea Hotel in Abuja, Nigeria on September 20 2007. There were more than 70 participants from FMAWR, other ministries and agencies, farmer organizations, development partners, academia, and the private sector (see appendices A and B for the agenda and participants list). The workshop opened with remarks from Ms. Pepple, Permanent Secretary, FMAWR; Ms. Julia Bracken, Head of Cooperation, Canadian International Development Agency (CIDA); and Dr. Shenggen Fan, Division Director, IFPRI. This report provides a brief review of the presentations delivered during the workshop, key comments from the audience, and the next steps.
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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 | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".