Innovation and capacity building to support Afghanistan’s rural development: Input to the Afghanistan National Peace and Development Framework
Bibliographic record
Abstract
Afghanistan-ICARDA programs have field tested a range of rural development approaches and practices. Many of these are ripe for scaling-up at national level and can contribute to the EU-Afghanistan National Priority Programs (NPP) 2017-2021. The rural development plans of the Islamic Republic of Afghanistan have been supported by ICARDA – the International Center for Agricultural Research in the Dry Areas – since 2002, in areas including: provision of seeds and new crop varieties, improving management of land and water, introducing new agricultural production technologies and farming practices, and enterprise building for rural communities, with a special focus on women’s empowerment. These programs have helped develop the national agriculture sector – particularly in a number of remote areas – and the rebuilding the agricultural genetic diversity that was lost during the country’s conflict years. Development and research programs over the past two decades were funded by the European Union, Australia, USAID, IFAD, OFID, JICA (Japan), UKAID, The Netherlands, IDRC (Canada) and FAO.
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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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 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".