Will legal international rhino horn trade save wild rhino populations?
Bibliographic record
Abstract
Wild vertebrate populations all over the globe are in decline, with poaching being the second-most-important cause. The high poaching rate of rhinoceros may drive these species into extinction within the coming decades. Some stakeholders argue to lift the ban on international rhino horn trade to potentially benefit rhino conservation, as current interventions appear to be insufficient. We reviewed scientific and grey literature to scrutinize the validity of reasoning behind the potential benefit of legal horn trade for wild rhino populations. We identified four mechanisms through which legal trade would impact wild rhino populations, of which only the increased revenue for rhino farmers could potentially benefit rhino conservation. Conversely, the global demand for rhino horn is likely to increase to a level that cannot be met solely by legal supply. Moreover, corruption is omnipresent in countries along the trade routes, which has the potential to negatively affect rhino conservation. Finally, programmes aimed at reducing rhino horn demand will be counteracted through trade legalization by removing the stigma on consuming rhino horn. Combining these insights and comparing them with criteria for sustainable wildlife farming, we conclude that legalizing rhino horn trade will likely negatively impact the remaining wild rhino populations. To preserve rhino species, we suggest to prioritize reducing corruption within rhino horn trade, increasing the rhino population within well-protected 'safe havens' and implementing educational programmes and law enforcement targeted at rhino horn consumers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".