Training and Capacity Building: An Essential Strategy for Development at an International Research Center
Bibliographic record
Abstract
In order to be meaningful, agricultural research has to provide solutions to problems, especially in the international agricultural research system which is designed to contribute to enhanced food production and improved rural livelihoods in the lesser-developed world. Training and human resource development, whether at the technical support or research scientists/managerial level, is fundamental to an effective agricultural research and technology transfer system. By comparison with the developed world, the national agricultural research systems (NARS) in developing countries are weak, often with ineffective extension programs, as typified by the West Asia-North Africa (WANA) region, which is served by the International Center for Agricultural Research in the Dry Areas (ICARDA). Despite the potential benefits of enhancing human skills, training and human resource development activities are often under-valued and under-funded in international research centers that serve developing countries. By highlighting training at ICARDA and its mandate countries, we sought to give renewed focus on this important component of the mission of the Consultative Group on Agricultural Research (CGIAR). In this article, we considered ICARDA’s philosophy and concepts on training, collaborating institutions, educational materials, categories of training, development of training courses, significant outcomes of training, shifting paradigms, and future directions. ICARDA’s innovative collaborative approach is a model to be emulated not only by the Centers but by other international institutions involved in agricultural and rural development in the developing countries. At this crucial time of restructuring of the CGIAR, renewed emphasis on training has never been more urgent.
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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 | 0.060 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.008 | 0.040 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".