Urgent Policy Change Is Needed to Understand the Dimensions of Legal International Wildlife Trade to Enable Targeted Management
Bibliographic record
Abstract
ABSTRACT Wildlife trade is a key threat to global biodiversity, involving thousands of species and millions of individuals. Global research and policy attention on international wildlife trade has increased in recent years and is represented in key global policy frameworks (e.g., Kunming–Montreal Global Biodiversity Framework). Yet the dominant focus of research and discussion is on illegal wildlife trade and the use of CITES in managing trade for a subset of species, despite the fact that the majority of species in trade are legal and fall outside the remits of CITES. Furthermore, there is no global mechanism to record what species are traded; current systems only capture subsets of species and regions, with no consistent standards. This hampers our understanding of global trade patterns and limits any understanding of the wider sustainability of international wildlife trade. There is an urgent need to develop and implement policies that capture the full scope of international trade, tools that embed comprehensive and reproducible sustainability assessments, and funding that reflects the telecoupled nature of trade and the inherent wealth imbalance between exporting and importing nations. The adoption of these more holistic approaches is critical for a sustainable future for species in trade and the livelihoods reliant on them.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".