Legal freshwater turtle meat trade in Indonesia only marginally contributes to collectors’ livelihoods
Bibliographic record
Abstract
In Indonesia tens of thousands of freshwater turtles and tortoises are collected and traded annually for their meat, including a large proportion that is exported. Proponents of international wildlife trade often state that such international trade directly contributes to the livelihoods of numerous families, especially those at the source end of the trade chain, i.e., harvesters and collectors. We note, however, that surprisingly little direct evidence is available for this assertion. To better understand the value of such trade to the livelihoods of those at the source end of the trade chain we explored to what extend the legal harvest of these turtles for the domestic and international meat trade in Indonesia supports livelihoods. Harvest and trade of freshwater turtles and tortoises in Indonesia is regulated through an annually set province-by-province quota system, and annually around 50,000 turtles belonging to four species are allowed to be collected from the wild. For each province we calculated the total monetary value of turtles for collectors, and we divided this by the government’s recommended minimum wage for each province arriving at the number of collectors that can legally earn a minimum wage by collecting turtles. We find that, nation-wide, legal collection allows a maximum of between 241 and 360 collectors (0–41 per province) to gain a year-round minimum wage, without including costs for permits, transportation, equipment and other expenses. Given that the legal trade in turtles provides so few people with a minimal amount of income, and given the increasingly imperilled status of numerous freshwater turtle and tortoise species, and the decline in Indonesia’s freshwater turtle and tortoise species in particular, we question whether allowing this trade is an effective livelihood strategy and whether such unsustainable trade should continue to be promoted and allowed.
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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) | 0.001 | 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".