Management options for addressing the persistent and unresolved CITES issue of Madagascar’s rosewood stocks and stockpile
Bibliographic record
Abstract
Stocks and stockpiles of CITES (the Convention on International Trade in Endangered Species of wild fauna and flora) listed wildlife, including animal and plant-derived products, remain a complex, unresolved issue. The biggest challenges lie in the prevention of further illegal sourcing of—and trade in—products originating from wild populations of threatened species. Stocks can function as a buffer during lean periods or as a mechanism used for speculation. As we outline in this paper, the current situation in Madagascar precludes non-detriment findings intended to enable sustainable use of standing rosewood populations. Backed by the World Bank, the previous Malagasy government was in the process of promoting the sale of massive stocks and stockpiles of confiscated precious woods in order to reach a zero stocks goal, this being ostensibly to halt the illegal sourcing and trafficking of rosewood. We propose and analyse four potential options for stocks management by presenting a framework linking forest management with socio-economic objectives and comparative risks. Destruction (burning) of the known stocks would send out a strong conservation message and has the strongest chance of halting further sourcing, which happens mostly in protected areas and is therefore illegal. National trade is the option in which a precious timber sector would process the woods in stocks. This option is the most beneficial for development. Opening the stocks for exportation through international trade achieves the smallest number of objectives in relation to both forests and socio-economic indicators, but comes with the highest risks in terms of curbing further illegal logging. Banking represents a fourth option, which essentially postpones any decision related to stocks management by storing the stocks for extended periods. None of the four management options is able to ensure a sustainable solution that can resolve the issues surrounding the precious timber stocks. The approaches put forward are either just ‘more good’, or ‘less good’. If a country is seriously interested in conserving its biodiversity, any government has to ensure that no other sectorial changes will counteract, or potentially undermine, the efforts to protect the environment. Stocks management will be on agenda at the upcoming COP18 in Geneva, 17–28 August 2019.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".