Living with rodent pests: Unifying stakeholder interests to prioritise pest management in rural Madagascar
Bibliographic record
Abstract
Abstract Rodent pests can have major social, economic, and environmental impacts. Their management, therefore, represents a complex socio‐ecological problem involving a network of stakeholders from across different sectors, with diverging and sometimes competing interests. Failure to incorporate stakeholder interests can result in ineffective or unsustainable management programmes, with unintended negative consequences for people and nature. Participatory approaches to decision‐making have been proposed as suitable strategies to tackle complex problems, yet, these processes are often considered too difficult, costly, or time‐consuming to implement. To facilitate a participatory approach to rodent control in Madagascar, we identified and mapped key stakeholders and developed a multisector framework for guiding rodent management programmes based on current literature and expert recommendations. We then carried out interviews and focus groups with stakeholders and end‐users to validate the final framework. The final framework unifies stakeholder interests around the dimensions of People, Resources, Knowledge and Power. Combined application of the stakeholder map and framework provides decision‐makers with the tools to identify stakeholder interests; to explore areas of conflict, as well as areas of agreement; and to ensure that these are addressed within the design of control programmes. As an assessment tool, the framework can also be used to evaluate the responsiveness of programmes to the needs of different stakeholders and assess whether objectives are being reached. We recommend the application of the stakeholder map and framework to encourage and strengthen participatory approaches aimed at rodent pest control. Due to the inclusive and interdisciplinary nature of the framework, it can be applied to address numerous complex social, environmental, and economic issues across scales, sectors, and systems. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".