The Mozambican Experience in Institutionalizing Agrarian Research
Bibliographic record
Abstract
This work addresses the historical process of institutionalization of agrarian research in Mozambique as well as the main challenges that it faces. The study was based on secondary data that address agrarian research from its genesis to the present. The results indicate that in the period prior to Mozambique’s independence in 1975, the best agrarian research infrastructures were concentrated in the South of the country, the region with the lowest agricultural potential compared to the Centre and the North. With the independence, the Mozambican Agrarian Research Institute (IIAM), the largest national agrarian research institution, expanded the experimental stations to the Centre and North. However, due to the war that hit the country between 1976 and 1992, agrarian research was not very effective in this period. After the end of the civil war, IIAM and some institutions of higher education, especially Eduardo Mondlane University (UEM), developed several technologies to increase agricultural productivity. Currently, the challenges of agrarian research are enormous, specially the need to: increase the quantity and quality of researchers, study the impact of climate change on agriculture, increase funding for research by government and other national partners, study the causes of the discontinuation of the use of improved agricultural technologies as well as the inclusion of farmers as priority subjects in agrarian research. Improving the linkage between research and rural extension is crucial for the generation and diffusion of appropriate agricultural technologies to the reality of Mozambican farmers.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".