Reconsidering the “War on Rats”: What We Know From Over a Century of Research Into Municipal Rat Management
Bibliographic record
Abstract
To sustainably control urban rat infestations, management efforts need to encompass large areas of urban centers. Therefore, the objective of this review and narrative synthesis was to collate what is known about municipal-scale rat management. We explored the management frameworks that have been used at a large scale in cities and we describe the expectations of experts who have designed and implemented these frameworks. We found that there has been a persistent “war on rats” paradigm driving this literature since the early 1900s. Not only was there little quantitative evidence to support this paradigm and associated methodologies, but together, they failed to meet the expectations of those who designed and implemented them due to real-world constraints (i.e., limited resources). To improve the field of municipal management, we identify two distinct options. First, stakeholders may continue to wage the “war on rats” while improving existing strategies within this paradigm. Key pathways forward include developing evaluation metrics aligned with program objectives, establishing the cost-effectiveness of methodologies, and improving control efficacy. Second, we suggest a new paradigm, one that considers that rat management is a complex system that must be approached by first mapping its complexity to highlight and prioritize the many complex upstream determinants of infestations. This perspective highlights how rat management may be a wicked problem for which there is no overarching solution and instead can only be managed by making incremental gains in individual components of the problem over time. Importantly, we propose an alternative management objective that places a priority on improving the overall health of the community, instead of on eliminating rats, and we stress that management goals must address stakeholders’ goals as their investment is essential for a sustained program.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".