A Framework for Assessing the Suitability of Different Species as Companion Animals
Bibliographic record
Abstract
Abstract Municipal regulations and humane movement policies often restrict or discourage the use of ‘exotic’ species as companion animals. However, confusion arises because the term ‘exotic’ is used in various ways, and because classifying species as exotic or non-exotic does not satisfactorily distinguish suitable from unsuitable companion animals. Even among commonly kept species, some appear to be much more suitable than others. Instead, decisions about suitable companion animal species need to be based on a number of relevant issues. As ethical criteria, we considered that keeping a companion animal should not jeopardize - and ideally should enhance - its welfare, as well as that of its owner; and that keeping a companion animal should not incur any appreciable harm or risk of harm to the community or the environment. These criteria then served as the basis for identifying and organizing the various concerns that may arise over keeping a species for companionship. Concerns include how the animals are procured and transported, how well their needs can be met in captivity, whether the animal poses any danger to others, and whether the animal might cause environmental damage. These concerns were organized into a checklist of questions that form a basis for assigning species to five proposed categories reflecting their suitability as companion animals. This assessment framework could be used in creating policy or regulations, and to create educational and decision-making tools for pet retailers, animal adoption workers, and potential owners, to help prevent animals from being placed in unsuitable circumstances.
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 | 0.050 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.018 | 0.007 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".