A survey of urban Canadian animal control practices : the effect of enforcement and resourcing on the reported dog bite rate
Bibliographic record
Abstract
Dog bites are a serious cause of human injury. Data from the United States of America indicate that about 1.8% of people receive bites each year, and the rate is thought to be increasing disproportionately faster than the dog population. The Canadian dog bite issue is not well documented although it is a public safety concern. Increased enforcement, education and breed-specific legislation have been used in attempts to lower dog bite incidence. However, the decentralization of the animal control system in Canada, the lack of standardized terms, and the lack of mandated reporting have resulted in unreliable information from which to evaluate the effectiveness of the strategies. The purposes of this exploratory study were to identify the reported dog bite rate in urban Canadian jurisdictions and to identify animal control strategies that may effectively reduce dog bite incidence. Thirty-six municipalities responded to a questionnaire about animal control resourcing, enforcement, and reported dog bites. The study found a median of 1.8 reported dog bites per 10,000 people, far below the number of “non-household” dog bites (bites caused by dogs not known to the victim) expected on the basis of other studies. Enforcement activities varied widely between municipalities, with some distinct regional differences. Contrary to initial expectations, the reported dog bite rate was positively correlated with most indicators of enforcement. This positive correlation is probably due to a greater proportion of bites being reported in municipalities with more active enforcement. However, municipalities with very high ticketing rates had far fewer reported dog bites than would be expected based on the linear relationships. The results are best explained by a regression of reported dog bites on ticketing rate consisting of a positive linear component and negative quadratic component. Increased ticketing appears to have increased the reporting rate (causing a positive linear regression) and reduced the actual rate of biting (causing a negative quadratic regression), at least when enforcement was high. Within the limitations of this study, the data provided no evidence to suggest that breed-specific legislation is effective in reducing the rate of reported dog bites.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".