Socio-ecological challenges and food security in the ‘salad bowl’ of Fiji, Sigatoka Valley
Bibliographic record
Abstract
Abstract This article examines food security in the Sigatoka Valley, one of the most productive food regions in Fiji, in the context of recent socio-ecological challenges through a case study of Narewa village. Data were collected using semi-structured interviews ( n = 25), a fixed question food insecurity experience survey ( n = 25), and a free listing exercise about preferred and consumed foods ( n = 24). Results revealed that while most households had access to sufficient food, the increased frequency and intensity of droughts, tropical cyclones, and flooding caused almost half to worry about meeting their future food needs. To date, a culture of sharing within the village has helped most households access food but this will likely be inadequate to meet future needs as climate change is projected to impact food production. Given that the foundation of food production in Narewa, like other villages in the valley, relies on the long-term viability of agricultural systems, better focus needs to be placed on the natural resources that form the backbone of these systems such as water availability, soil health, and slope stability and their resilience to anthropogenic and natural stressors. Efforts that focus on protecting and enhancing local ecosystems in light of expected future climate change, combined with greater attention on food storage and the use of resilient crops, and enhancing social cohesion and sharing networks are needed to avoid breaching tipping points in the food system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".