Canada and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES): Lessons Learned on Implementation and Compliance
Bibliographic record
Abstract
Unsustainable and illegal wildlife trade are contributing to the unprecedented levels of biodiversity loss and possible extinction of one million species. Law enforcement and the criminal justice system have a role to play in helping to regulate and monitor such trade. The main international instrument to regulate wildlife trade is the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). This mixed methods study researched the lessons learned and best practice in regards to implementation of and compliance with CITES. As part of the study, three countries were identified as case studies and Canada was selected as one of these. Lessons can also be learned from Canada's Wild Animal and Plant Protection and Regulation of International and Interprovincial Trade Act, which is cumbersome to update when species protections change within CITES. Canada has several elements of good practice, such as the remit, effectiveness and relationships of the three CITES authorities located within Environment and Climate Change Canada, the public health approach to some wildlife imports, and the protection of native CITES species. CITES needs to be improved to further protect endangered species and lessons from Canada and other countries can contribute to this improvement.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".