Characteristics of dog training companies and their services in British Columbia, Canada
Bibliographic record
Abstract
The information about dog training companies available on their websites varies in quality and clarity, which can impact how guardians choose a trainer. We collected and analysed information from public websites of dog training businesses in British Columbia (B.C.), Canada to learn about the training credentials, services, modes, and methods used. We recorded the number of trainers, gender, and training credentials of the lead trainer(s), training methodology, modes and types of training services, and whether other pet services were offered. We found 281 businesses operating in the province, and 72.24% were identified as using only reward-based training methods. It was more likely for reward-based training businesses than aversive-based businesses to list training credentials on their websites [X2 (1, N = 244) = 28.74, p <.001]. Most companies (98.22%) provided easily accessible information about the modes and type of training services offered. Online training was offered by 34.16% of companies. In terms of gender, and in line with previous research, we found that women outnumbered men as lead trainer. Women trainers were also more likely to employ reward-based methods [X2 (1, N = 264) = 20.19, p <.001] and to hold training credentials [X2 (1, N = 264) = 13.91, p <.001]. These findings provide a benchmark for future studies, as well as inform animal welfare advocacy and social change programs that aim to influence dog guardians to choose only reward-based trainers.
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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 | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".