Stakeholder perspectives on the development and implementation of approaches to municipal rat management
Bibliographic record
Abstract
Abstract Rats evoke public health and economic concern in cities globally. Rapid urbanization exacerbates pre-existing rat problems, requiring the development and adoption of more effective methods of prevention, monitoring and mitigation. While previous studies have indicated that city-wide municipal management approaches often fail, such outcomes are often left without specific explanation. To determine how municipalities could more effectively develop and implement large-scale approaches, we interviewed stakeholders in municipal rat management programs to document their opinions, recommendations and the challenges they face. Using a thematic framework method, this study collates and analyzes in-depth interviews with 39 stakeholders from seven cities across the United States. Overall, stakeholders’ recommendations for municipal rat management aligned with many conceptual attributes of effective management reported in the literature. Specifically, stakeholders highlighted the need to prioritize the reduction of resources available to sustain rat infestations (e.g. food, water and harborage), to focus on proactive (vs. reactive) measures, and to implement large-scale data collection to increase the efficiency of cross-city rat control. Stakeholders also suggested novel approaches to management, such as mitigating rat-associated risks for vulnerable populations specifically and developing tailored initiatives based on the specific needs and desires of residents. We synthesize these recommendations in light of reported barriers, such as resource limitations, and consider several opportunities that may help municipalities reconceptualize their approaches to city-wide rat management.
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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) | 0.001 | 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".