Turning Negatives into Positives for Pet Trading and Keeping: A Review of Positive Lists
Bibliographic record
Abstract
The trading and keeping of exotic pets are associated with animal welfare, conservation, environmental protection, agricultural animal health, and public health concerns and present serious regulatory challenges to legislators and enforcers. Most legislation concerning exotic pet trading and keeping involves restricting or banning problematic species, a practice known as "negative listing". However, an alternative approach adopted by some governments permits only the keeping of animals that meet certain scientifically proven criteria as suitable in respect of species, environmental, and public health and safety protections. We conducted an evaluation of positive lists for the regulation of pet trading and keeping within the context of the more prevalent system of restricting or prohibiting species via negative lists. Our examination of international, national, and regional regulations in Europe, the United States, and Canada found that criteria used for the development of both negative and positive lists were inconsistent or non-specific. Our online surveys of governments received limited responses, although telephone interviews with officials from governments either considering or developing positive lists provided useful insights into their attitudes and motivations towards adopting positive lists. We discuss key issues raised by civil servants including perceived advantages of positive lists and anticipated challenges when developing lists of suitable species. In addition, we compare functions of negative and positive lists, and recommend key principles that we hope will be helpful to governments concerning development and implementation of regulations based on positive lists.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".