Urban rats are the ‘fall-guy’: Resident motivations for municipal rat complaints
Bibliographic record
Abstract
Rats are an important issue in cities globally. Despite their ubiquity, perceptions and concerns about rats vary with circumstance and the context in which a person interacts with them. Municipal rat management programs are a service to communities and therefore must be responsive to the varied concerns of their residents. Understanding why communities are concerned about rats can help inform rat management programs to meet the specific needs of their residents. The objective of this study was to identify why the residents of Vancouver, Canada care about rats and what they want done to address them. To do this, we qualitatively analyzed 6,158 resident complaints about rats made to the city's municipal government between January 2014 and May 2020. Using a qualitative descriptive coding process, we found that rats were a priority in a minority of cases. In general, people were more concerned about broader community issues, such as neighborhood disorder, of which rats were one part. Complaints tended to be made when problems were highly visible, nearby, and when the complainant wanted the city to take action to alleviate this issue, particularly when they were in and around their living spaces. The rates of complaints were highest in the most economically and socially deprived neighborhoods and lowest in the most privileged neighbourhoods. We synthesize this information with a view towards understanding how to develop objectives and actions for municipal management strategies that are grounded in community concerns.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".