Guidance on stakeholder engagement practices to inform the development of area-wide vector control methods
Bibliographic record
Abstract
Introduction and purposeThese recommendations on practices for stakeholder engagement build on the knowledge and experience of practitioners and subject-matter experts from a large variety of fields.They aim to provide teams involved in the development of area-wide vector control methods with guidance on how to design community and stakeholder engagement programmes as part of their development pathway.Area-wide vector control methods are not new in concept [1].For example, the use of natural predators for biocontrol of agricultural pests or public intervention for the treatment of water bodies with larvicides are current area-wide applications.Thus, new approaches under development-such as those using sterile-male techniques, Wolbachia, or gene drive-can build upon well-established development pathways.These methods offer the benefit of providing vector control for all inhabitants of a specific treated area without individual or group biases related to economic means, level of education, etc.Although this benefit can be a great advantage, multiperson or community-applied interventions may not offer individuals the chance to 'opt out' of a home or area receiving the intervention [2].There may be aspects of the research in which individuals can choose to participate or not (for example, during the collection of mosquitoes or other insects from houses), but the research or technique developed will, at some stage, require the deployment of the tools in selected sites, and residents of those sites may not be able to 'opt out' of these phases in the same way that individuals can decline to be part of a vaccine field trial.Therefore, although these methods may differ greatly in scope and impact, the processes for their development and use share commonalities that provide a basis for asserting a broadly applicable framework for
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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 | 0.329 | 0.348 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".