Food security and climate change from a systems perspective: community case studies from Honduras
Bibliographic record
Abstract
Food security is described as a condition in which all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life (World Food Summit, 1996). Climate variability and climate change can affect all aspects of food security, but past impact assessments have focused primarily on agricultural production. Efficient responses require an understanding of the full spectrum of potential climate impacts on food utilization, access and availability, as well as on the underlying natural, built and governance systems. In this paper, we apply a broader systems approach to evaluating food systems resilience in the context of climate change (Bizikova, Tyler, Moench, Keller, & Echeverria, 2015) to 10 communities in Honduras. The results indicate that resilience building depends on a sound understanding of how communities access food and how climate impacts can cascade through different parts of the food system. Key support systems, such as natural resources, storage, transportation and energy have to be strengthened, and local governance has to be preserved and improved. These considerations should be integrated into the development and implementation of relevant policies and measures at different levels of decision-making.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".