The Business Of Urban Animals Survey: the facts and statistics on companion animals in Canada.
Bibliographic record
Abstract
At the first Banff Summit for Urban Animal Strategies (BSUAS) in 2006, delegates clearly indicated that a lack of reliable Canadian statistics hampers municipal leaders and legislators in their efforts to develop urban animal strategies that create and sustain a healthy community for pets and people. To gain a better understanding of the situation, BSUAS municipal delegates and other industry stakeholders partnered with Ipsos Reid, one of the world's leading polling firms, to conduct a national survey on the "Business of Urban Animals." The results of the survey, summarized in this article, were presented at the BSUAS meeting in October 2008. In addition, each participating community will receive a comprehensive written analysis, as well as a customized report. The online survey was conducted from September 22 to October 1, 2008. There were 7208 participants, including 3973 pet and 3235 non-pet owners from the Ipsos-Reid's proprietary Canadian online panel. The national results were weighted to reflect the true population distribution across Canada and the panel was balanced on all major demographics to mirror Statistics Canada census information. The margin for error for the national results is 1/- 1.15%.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".