Owner approaches and attitudes to the problem of lead-pulling behaviour in pet-dogs.
Bibliographic record
Abstract
This study aims to describe approaches and attitudes of UK and Ireland pet-dog owners to lead-pulling prevention and modification. <br/>In February/March 2019, UK and Ireland pet-dog owners, aged over 18, were recruited online, via dog-related and non-dog-related Facebook pages. Respondents completed a four-part questionnaire, exploring owner/dog demographics, walking practices, training and attitudes to lead-pulling, for one dog, owned for over thirty days. A data subset from a broader analysis of lead-pulling and pet-dog welfare, is presented herein. <br/>Of 2,531 respondents, 82.7% (n=2,092) of dogs pulled on lead. Over the 30-day study period, 32.1% of dogs that pulled were walked for ≤ 30 minutes daily and 18.2% were not walked every day. Although equipment to prevent pulling was popular [back-connection harnesses (43.1%), head-collars (7.4%) and front-connection harnesses (11.2%)], flat-collars were the most frequent equipment choice (59%).<br/>Of dogs that pulled, 63% had attended training classes, [puppy classes (21%), other classes (13.3%), multiple classes (28.7%)]; 85.3% of which included loose-lead exercises. (Of all the owners who answered) Owners favoured reward-based training for lead-pulling modification [i.e. praise (91.2%), food (72%)]; which was also deemed most successful. Nevertheless, aversives [i.e. pulling back on-lead (33%), lead corrections (16.4%)] were common and 25% of owners considered these Very/Extremely successful. Owners believed lead-pulling dogs want to take charge (21.7%), need stronger pack leaders (17.6%), will grow out of it (13.5%), are dominant (11.5%) or stubborn (11.5%). This study suggests that while humane methods of lead-pulling prevention and modification are being adopted, aversives are still commonplace. Furthermore, misconceptions regarding dog’s motivations for lead-pulling persist. <br/>
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".