Descriptive Texts in Dog Profiles Associated with Length of Stay Via an Online Rescue Network
Bibliographic record
Abstract
To increase the public’s awareness of animals needing homes, PetRescue, Australia’s largest online directory of animals in need of adoption, lists animals available from rescue and welfare shelters nationwide. The current study examined the descriptions accompanying online PetRescue profiles. The demographic data and personality descriptors of 70,733 dogs were analysed for associations with LOS in shelters—with long stays being a potential proxy for low appeal. Univariable and multivariable general linear models of log-transformed LOS with personality adjectives and demographic variables were fitted and the predicted means back-transformed for presentation. Further analyses were conducted of a subset of the dataset for the four most common breeds (n = 20,198 dogs) to investigate if the influence of personality adjectives on the LOS differed by breed. The average LOS of dogs was 35.4 days (median 18 days) and was influenced by several adjectives. Across all breeds, the LOS was significantly shorter if the adjectives ‘make you proud’, ‘independent’, ‘lively’, ‘eager’ and ‘clever’ were included in the description. However, the LOS was longer if the terms ‘only dog’, ‘dominant’, ‘sensitive’ and ‘happy-go-lucky’ were included in the description. Some of the association of descriptors with relatively long LOS are difficult to explain. For example, it is unclear why the terms “obedient” and trainable” appear unappealing. The confidence adopters have in these terms and their ability to make the most of such dogs merits further exploration. As expected, the LOS differed in different breeds with the Labrador retrievers having the fastest adoption rate among the most common four breeds with an average LOS of 14.5 days. Breed had interactions with four personality adjectives (gentle, active, quiet and energetic) indicating that the adoption rate of dogs with these descriptors in their online PetRescue profiles differed by breed. This highlights an important knowledge gap, suggesting that potential adopters have differing expectations according to the breed being considered. Increased awareness of the breed-specific influence of personality adjectives on appeal to potential adopters, may enhance adoption success by allowing dogs with risk factors for low appeal to be promoted more intensively than high-appeal dogs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".