EcoHealth approach to control of zoonotic emerging infectious diseases in Southeast Asia: Sharing the experiences
Bibliographic record
Abstract
The International Livestock Research Institute was established in 1994 through a merger of two livestock research centres belonging the Consultative Group on International Agriculture Research, leading to an expanded mandate. ILRI’s predecessors started out with a geographical focus on East Africa but expanded to other regions: including Southeast Asia. Always keen to test a novel approach, researchers learned of the EcoHealth approach that had been pioneered in Latin America and submitted a proposal entitled an ‘Ecosystem Approaches to the Better Management of Zoonotic Emerging Infectious Diseases in SE Asia’ was accepted for funding by the International Development Research Centre (IDRC, Canada). ILRI’s role throughout the project is a mentoring one. After identifying partners from various disciplines, research teams were formed and requested to reach an internal consensus of what zoonosis(-es) they would research. All EcoZD teams successfully devised their own research proposals. To measure incorporation of EcoHealth into research - outcome mapping and ‘EcoHealth Uptake’ were used. EcoHealth resource centres were also established. All teams have presented their research at international conferences and are expected to publish in peer-reviewed journals as well as local language ones. They have already or are preparing to feedback results to the communities where data was collected and engage with policy-makers. A number of these outputs will provide excellent reference case studies for other EcoHealth initiatives The challenges were many however: scope and ‘loose’ definitions of EcoHealth make it challenging to teach as a standard. In terms of building capacity a larger than anticipated effort was required to develop ‘intra-disciplinary’ skills such as proposal writing study design and analysis. EcoZD will be able to provide evidence that researchers can collaborate and produce research, policy and dissemination outputs.
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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 | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".