Small dogs display more aggressive behaviour than large dogs in social media videos
Bibliographic record
Abstract
Due to potentially greater vulnerability to accidents and attacks, the behaviour of small dogs may reflect greater threat sensitivity and need for protection than that of large dogs. Based on this hypothesis, I predicted that dogs of small breeds (<10 kg) would be more likely to show signs of aggression, stress, submission and attention-seeking behaviour than dogs of large breeds (>15 kg). I extracted behavioural data from 310 videos posted on YouTube depicting adult dogs of four small dog breeds (Chihuahua, Jack Russell Terrier, Dachshund and Yorkshire Terrier) and four large dog breeds (German Shepherd Dog, Border Collie, Labrador Retriever and Rottweiler; n=20 dogs/breed; mean±SE video duration: 59.8±2.0 s). Search terms included the breed name (n=160) for the control group, and the breed name with ‘angry’ (n=150) for the “angry” group. Behaviour in each video was scored using 1-0 sampling and effects of body size, breed, group and location were analysed by generalised linear models. Small dogs were more likely to show more total aggression (sum of vigilant, tail up, baring teeth, short bark, repeated barking, growling, snapping, biting: 1.9±0.13 vs 1.6±0.104; p=0.034), than large dogs. Snapping and biting incidents occurred in more of the small dogs, than the large dogs (0.22±0.05 vs 0.05±0.02; p=0.002). There were no differences between small and large dogs when it came to showing stress-related behaviour (sum of eye white, blinking, lip licking, trembling, panting, yawning, licking own body, ground sniffing, scratching), submissive behaviour (sum of looking away, withdrawing, tail down, paw lifting, presenting belly), and attention-seeking behaviour directed towards the handler (Sum of face licking, whimpering, paws on body, play invite, body licking, tail wagging, jumping). Small dogs were more likely to be on an elevated surface (including being held in a person’s arms) than large dogs (0.35±0.06 vs 0.08±0.03; p=<0,001), and reaction-provoking actions by the handler (teasing the dog, hovering hands above the dog’s head and moving camera close to dog’s face) were not different between large and small dogs, although handlers were more likely to touch small aggressive dogs, than large aggressive dogs (0.2±0.05 vs 0.07±0.02; p=0.002). These findings suggest that the observed behavioural differences between small and large dogs were mediated by differences in the behaviour of humans towards the dogs, leading to escalated aggressive behaviour in the small dogs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".