International, Transdisciplinary, and Ecohealth Action for Sustainable Agriculture in Asia
Bibliographic record
Abstract
Sustainably intensifying agriculture to secure food for people, while minimizing the human, animal, and environmental health impacts is an unprecedented global food security challenge. Action research is needed to understand and mitigate impacts, with Ecosystem approaches to health (Ecohealth) emerging as a promising framework to support such efforts. Yet, few have critically examined the application of Ecohealth principles in an agricultural context, particularly in Southeast Asia where agricultural intensification is rapidly expanding. In this paper, we evaluate the strengths, challenges, and opportunities of agriculture-related Ecohealth projects in low-resource settings of Vietnam, Thailand, Indonesia, and China, drawing on a case study of the Field Building Leadership Initiative (FBLI). To do this, we used a developmental evaluation framework involving several iterative cycles of document reviews, interviews, focus groups, and outcome harvesting with researchers, partners, and community members involved in FBLI. Results highlight the importance of transdisciplinarity, participation, and knowledge-to-action principles in co-generating knowledge and co-developing practical solutions. Implementing such principles presents challenges in terms of coordinating regional collaborations, managing high workloads, meaningfully engaging communities, and ensuring ongoing monitoring and evaluation. To address these challenges, there is a need to strengthen capacity in integrated approaches to health, improve institutionalization of Ecohealth, foster community engagement, and systematically monitor and evaluate efforts. Ecohealth holds significant promise in improving food security, but only when considerable time is spent developing and implementing projects with communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".