Articulating ethical principles guiding Target Malaria's engagement strategy
Bibliographic record
Abstract
Progress in gene drive research has engendered a lively discussion about community engagement and the ethical standards the work hinges on. While there is broad agreement regarding ethical principles and established best practices for conducting clinical public health research, projects developing area-wide vector control technologies and initiating ambitious engagement strategies raise specific questions: who to engage, when to engage, and how? When responding to these fundamental questions, with few best practices available for guidance, projects need to reflect on and articulate the ethical principles that motivate and justify their approach. Target Malaria is a not-for-profit research consortium that aims to develop and share malaria control and elimination technology. The consortium is currently investigating the potential of a genetic technique called gene drive to control populations of malaria vectoring mosquito species Anopheles gambiae. Due to the potentially broad geographical, environmental impact of gene drive technology, Target Malaria has committed to a robust form of tailored engagement with the local communities in Burkina Faso, Mali, and Uganda, where research activities are currently taking place. This paper presents the principles guiding Target Malaria's engagement strategy. Herein the authors (i) articulate the principles; (ii) explain the rationale for selecting them; (iii) share early lessons about the application of the principles. Since gene drive technology is an emerging technology, with few best practices available for guidance, the authors hope by sharing these lessons, to add to the growing literature regarding engagement strategies and practices for area-wide vector control, and more specifically, for gene drive research.
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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 | 0.215 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.053 |
| Scholarly communication | 0.029 | 0.016 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.035 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".