Dog breed selection and factors that shape them : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Zoology at Massey University, Palmerston North, New Zealand
Bibliographic record
Abstract
The aim of this research was to describe human perceptions of dog breeds, New Zealand national dog demographics, and the relationship between aesthetic appeal and physical conformation of dog breeds. Methods included a literature review, a review of New Zealand dog registration data, and a survey of 131 university students from first and third year veterinary science and first year marketing on the relative appeal of unmodified and modified dog images. \nBy reviewing literature on human preferences towards dog characteristics breeds were selected that would be most likely to generate the ideal positive and ideal negative first impressions. Characteristics were examined by compiling the strongest positive and negative preferences, opinions, and reports. The results indicated that the ideal breed for a positive impression would be a Labrador Retriever of pale or yellow colour. The ideal breed for the negative impression was Rottweiler. The German Shepherd Dog was also notable for creating a negative impression. \nThis study used datasets from the New Zealand National Dog Database (NZDD) (2013-2014) and New Zealand Kennel Club (NZKC) (2005-2014) to describe the New Zealand dog population. Results highlight a large difference between the two datasets in regards to rankings and reporting. The NZDD and NZKC top 10 ranked purebreds differed in that the NZDD top 10 contained more working breeds that are utilized in livestock farming (e.g. Huntaway). According to the NZDD data, most dogs in New Zealand are purebred (over 65%). The Labrador Retriever was the most commonly registered breed in both datasets. The kennel club data can be used for pedigree dog information but, unlike the NZDD, not national demographic information. \nThe study also investigated, using a survey with associated image ranking, whether academic programme or year of university study influenced the scoring of different dogs based on their physical appeal. The breeds presented in image sets (original and altered) were Belgian Shepherd (Malinois), Border Collie, Dachshund, French Bulldog, German Shepherd (Alsatian), and Jack Russell Terrier. Neither academic programme nor year of university study influenced scoring of five of the six image sets (all but the French Bulldog). Results from the French Bulldog image set indicated fourth year veterinary science students found the images with less exaggeration more appealing than either first year group. Also female participants preferred less exaggeration compared to male participants. For all six breeds the less exaggerated variants within the set of images were considered more appealing by all participants. These findings indicate that there was a preference among the students surveyed for dogs with physical characteristics that were less exaggerated and potentially less detrimental to the health and welfare of the animal.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
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".