Agriculture Sustainability, Inclusive Growth, and Development Assistance: Insights from Tanzania
Bibliographic record
Abstract
Aid for agriculture development in sub Saharan Africa has increased in recent years, but little is known about which farmers are participating in the interventions, the production structures employed, and the foreseeable consequence on food security and agricultural development. This research draws insights from two projects in Tanzania funded by the United States under its Feed the Future initiative. The research examined the categories of farmers participating, production structure employed, and the implications for sustainable and inclusive food security and agricultural development. The research reveals that significant results of sustainable production can be found at individual level, but only a limited number of farmers with endowment of suitable land with access to water, and credit and some level of organization are participating. The UN rapporteur on the Right to Food has called for increasing food production where the poor and hungry live; we argue that current investment approaches oriented to increasing production, fail to adequately address the local specificity of hunger. As a result, substantial increases in aid inflows over the recent years may have limited effect on reducing the numbers of the hungry. The challenge to stakeholders is to spread the technologies to many more smallholder producers, particularly targeting the poor more precisely.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".