Connecting the dots: a qualitative study of dog-bite data in Calgary (AB, Canada)
Bibliographic record
Abstract
IntroductionWorldwide, dog-bites remain a leading cause of pediatric injuries. Local governments are key because they can enact and enforce policies on dogs. The City of Calgary has earned an international reputation for its approach to regulating dogs and dog-owners, which has resulted in marked reductions in per capita dog-aggression complaints. Objectives and ApproachThis presentation reflects on how data on dogs are collected, sorted and used by local governments. Our approach has been qualitative in nature: we observed bylaw officers during ride-alongs, and we conducted in-depth interviews with officials who conceived and/or use the databases involving dogs in Calgary. We took a particular interest in the migration of the City of Calgary’s ‘canine data’ (e.g., dog licensing, reports by enforcement officers) to a more comprehensive database listing all of the incidents recorded by municipal services (including ‘911’ and ‘311’ calls, etc.). ResultsPreliminary results point to difficulties in linking data stemming from several sources. Within the municipality itself, the transition comes with important challenges. Moreover, to improve public health surveillance for dog-bites, and ultimately to improve preventive strategies, other sources of data should be linked, including emergency services, animal welfare charities, and and veterinary clinics. This fragmentation of available data would be difficult to overcome. Local governments and animal welfare charities may cooperate during investigations but do not share their administrative data. The issue of privacy is a strong barrier for municipal services to obtain healthcare data, while veterinary clinics are private entities that have few incentives to align with administrative data held by local governments or healthcare services. Conclusion/ImplicationsIn a city whose model is built upon the close monitoring of pets and their people, the issue of data linkages is critical. New partnerships and new solutions, respectful of citizen privacy and organizations’ respective scope of practice, should be developed.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.030 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".