Public perception of temperament in dogs may be influenced by working roles
Bibliographic record
Abstract
There are multi-factorial influences on people’s perception of the temperament of dogs, including a dog’s perceived role and abilities. Using a 5 point Lickert scale (very friendly – very aggressive), 463 students rated 3 dogs (Labrador, German shepherd and Airedale terrier) pictured alone and with 15 different categories of male and female handler, including assistance dog users, police, pet-owner and rough person. Identical photographs of each dog were used. Overall the Labrador was rated as the friendliest. Dogs alone were rated as less friendly than when with a handler [Labrador (F(14,462)=8.589 P=.000), German shepherd (14,462)=6.513 P=.000), Airedale terrier (F(14,461)=7.587 P=.000)]. The type of handler also influenced the rating. Independent of handler, gender or breed of dog, dogs portrayed as assistance animals were rated as significantly more friendly than when portrayed as a police dog or owned by a rough individual. Conversely police dogs were rated as significantly less friendly. The interesting point is that considerable significant mean differences in rating of handlers appear when adjusted for the (a) effect of the handler on the dog (b) the effect of prior rating of the dog alone on the rating of the dog with a handler and (c) the effect of a handler on rating the dog when controlling for effect of prior rating of the dog alone. Full statistical analysis is available.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".