Protect the Integrity of CITES: Lessons From Japan's IWC Withdrawal to Keep Polarization From Tearing CITES Apart
Bibliographic record
Abstract
ABSTRACT Unsustainable wildlife trade is a major driver of global biodiversity loss. Effective wildlife trade governance is critical for conservation and requires international cooperation and coordination to regulate an industry valued at hundreds of billions of dollars a year. Yet, due to increasing polarization over consumptive wildlife use, certain countries have become disenfranchised by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), the primary mechanism for regulating international wildlife trade. Tensions within CITES are rising over the elephant ivory and rhino horn trade, where polarization has pushed ten Southern African Development Community countries to suggest an outright withdrawal from CITES. The denunciation of CITES by such a large and ecologically significant bloc would substantially weaken the integrity, credibility, and stature of the Convention. There is a contemporary precedent to reference: Japan left the International Whaling Commission (IWC) in 2019 due to polarization over commercial whaling. Here, we examine the common threads between these two cases: changing organizational ethos, polarization amongst members, influence of non‐state actors, and loss of decidability for dissenting nations. Taking critical lessons from Japan's IWC withdrawal, we propose various options for structural reforms in CITES to restore decidability, enable equitability, and implement inclusive decision‐making.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".