Understanding Community Perceptions of the St. Kitts’ “Monkey Problem” by Adapting Harm Reduction Concepts and Methods
Bibliographic record
Abstract
Wicked problems in One Health are associated with dynamicity and uncertainty that require experts, authorities and community members to reach for innovative means of collective inquiry, and collaborative interventions to address the deep social issues at the root of interspecies problems. In this study we explore the value of harm reduction concepts to understand a hundreds of year old issue, the St. Kitts’ “monkey problem,” which involves the invasive African green monkey ( Chlorocebus sabaeus ) as the cause of deleterious effects on agriculture, but concurrent positive effects on tourism and biomedical research. The harm reduction approach, a systems and settings-based approach with decades of success in public health, can serve as a framework to produce action on persistent societal problems. Harm reduction concepts and methods and participatory epidemiology were used to uncover local perceptions about human-monkey interactions and “meet people where they are” by asking the research question: Are there commonalities in perceptions and values linked to the St. Kitts’ “monkey problem” that are shared across diverse representatives of society that can act as a common starting place to launch collaborative responses to this invasive species? Through a series of focus group activities and interviews we found that the Kittitian “monkey problem” is a contentious and dichotomous problem pervasive in most of society that has no single stakeholders nor one solution. Harm reduction helped to map the island’s human-monkey system and elucidated an entry point toward tackling this problem through the identification of shared values, and also provided a model for incremental gains that may be achieved. Likening the St. Kitts “monkey problem” to a wicked problem enabled stakeholders to seek more options to manage the problem rather than to conclusively solve it. Frequently mentioned shared values including the protection of farmer crops and backyard harvests likely represent strong entry points to this problem and a jumping-off point to begin collective action toward future improvements.
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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 | 0.001 | 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 | 0.001 | 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".