When Arguments Prevail Over Power: The CITES Procedure for the Listing of Endangered Species
Bibliographic record
Abstract
The legitimacy and effectiveness of the Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES) depends on problem-adequate listing decisions. Decisions are frequently highly controversial, because they commit the member states to imposing trade restrictions on listed species. We examine whether—and how—CITES' impressive institutional apparatus deprives the member states of their bargaining power and empowers actors who can make reasoned arguments on the merits of a listing decision. For this purpose, we demonstrate theoretically that appropriately designed decision-making procedures can diminish stake-holders' opportunities for exploiting their bargaining power and provide room for reason-based deliberation. Subsequently, we explore member states' and other stakeholders' incentives, created by the CITES listing procedure, for refraining from bargaining and accepting scientifically sound decisions. Finally, we examine three recent controversial listing decisions as examples of the actual operation of the listing procedure.
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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 | 0.050 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".