Climate Change, Food Security, and Health: Harnessing Agroecology to Build Climate-Resilient Communities
Bibliographic record
Abstract
Climate change threatens human health, food security, and ecological sustainability. In marginalized and vulnerable communities around the globe, there is a crucial need to initiate actions to reduce adverse climatic impacts and support sustainable development goals (SDGs), particularly on food and health. Climate change’s multidimensional and complex impact on food and health has prompted calls for an integrated, science-based approach that could simultaneously improve the environment and nourish development-constrained communities. This paper examines a transdisciplinary practice of agroecology that bridges the gap between science, practice, and policy for climate action. We also analyze the significance of agroecology in building climate-resilient communities through sustainable food systems. We assert that the marriage of science and local knowledge that addresses access inequities through agroecology can lessen the impact of climate change on rural communities to achieve healthier, more sustainable, and equitable food systems. Furthermore, a transformative agroecological paradigm can provide farmers with a host of adaptive possibilities leading to healthier communities, improved food security, and restored lands and forests that can sequester greenhouse gases. Based on our findings, we call on the science and policy communities to integrate agroecology as part of the broader strategic approach to climate change adaptation and mitigation.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".