Realities of offering advice to governments on CITES
Bibliographic record
Abstract
What happens when those who provide conservation advice are required to take policy and management action based on that advice? Conservation advocates and scientists often try to prompt regulatory change that has significant implications for government without facing the challenge of managing such change. Through a case study, we placed ourselves in the role of the government of Thailand, facing obligations to seahorses (Hippocampus spp.) under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). These obligations include ensuring that its exports of seahorses do not damage wild populations. We applied a CITES-approved framework (which we developed) to evaluate the risks of such exports to 2 seahorse species. We used the framework to evaluate the pressures that put wild populations of the species at risk; whether current management mitigates the risk or offsets these pressures; and whether the species is responding as hoped to management policy. We based our analysis on information in published and grey literature, local knowledge, citizen science data, results of government research, and expert opinion. To meet CITES obligations, exports of both species would need to be prohibited until more precautionary adaptive management emerged. The risk of any exports of Hippocampus trimaculatus was above a tolerable level because of a lack of appropriate management to mitigate risks. In contrast, the risk of any exports of Hippocampus kuda could become tolerable if monitoring were put in place to assess the species' response to management. The process we developed for Authorities to determine risk in response to CITES guidelines was challenging to implement even without the need for government to consider social implications of conservation action. Despite the imperfections of our risk evaluation, however, it still served to support adaptive management. Conservationists need to keep implementation in mind when offering advice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".