Does the Role a Pet Played before Disposition, and How the Pet is Lost Influence Pet Owner’s Future Pet Adoption Decision?
Bibliographic record
Abstract
The purpose of the paper is to explore how the role a pet played before disposition and how the owner lost his/herpet affect the pet owner’s next pet adoption decision. Results from Pearson chi-square tests of independence showno significant relationship between how the respondent viewed his/her pet before relationship ended and the lengthof time he/she waited before adopting another pet. However, a significant relationship was found between how thepet owner lost his/her pet and the length of time he/she waited before adopting another pet. Respondents who saidthey lost their pet voluntarily were more likely to wait longer before adopting another pet than those who said theylost their pet involuntarily. Moreover, the results confirm that respondents who viewed their pet as a child, beforeend of relationship, were more likely to hold funeral rituals than those who viewed their pet as a friend, a family orhousehold member. Finally, those pet owners who lost their pets and decided to adopt another pet are likely tochoose a pet of the same species but different breed. Implications to theory and practice are discussed.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".