Identifying system archetypes in Nigeria’s rice agri-food system using fuzzy cognitive mapping
Why this work is in the frame
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Bibliographic record
Abstract
Nigeria is a major rice-producing and rice-importing country in Africa, challenged with ensuring rice-food security for its growing population. Successive governments have implemented several strategies to increase local rice production such as rice import restriction policies and agricultural investments. These strategies have yielded results but achieving long-term sustainable growth in Nigeria’s rice agri-food system has remained elusive. Addressing food security and sustainability in agri-food systems requires a systems-thinking approach. In this study, we applied two systems thinking techniques, fuzzy cognitive mapping (for describing the system structure and behavior) and archetype analysis (to reveal generic system archetypes and effective strategies to improve the system). Our analysis revealed three system archetypes: limits to success, fixes that fail, and drifting goals. Rice production is limited by low agricultural productivity indicating the “limits to success” archetype. Farmers tend to increase rice area as a “quick fix” to productivity issues but this quick fix leads to unintended consequences such as soil degradation (fixes that fail archetype). Additionally, because of the import-restriction policies generating an unmet demand for rice, the government may face pressure to lower the goal of self-sufficiency falling into the “drifting goals” archetype. However, our analysis shows that suspending import-restriction policies would result in undesirable system states, with reduced demand for local rice and lower rice production. Our results underscore the importance of government policies in increasing rice production sustainably and ensuring food security.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it