Wild Meat Trade Chain on the Bird's Head Peninsula of West Papua Province, Indonesia
Bibliographic record
Abstract
Wild meat has long been a staple of rural communities in West Papua Province, Indonesia. However, development of commercial networks has resulted in wild meat being increasingly transported in from forests to urban areas. This study describes the structure and operation of the wild meat trade chain along the coast of the Bird's Head Peninsula (BHP) of West Papua, including how wild meat flows from forests to urban areas and contributes to local livelihoods across the trade chain. Commercial networks have developed and wild meat is transported from forests to urban areas and contributes to livelihoods along the entire value chain. A survey was conducted on three groups of hunters: “focal respondents” (N = 220), including “participating hunters” (N = 33); “random respondents” (N = 800); and other actors (“intermediaries” [N = 6], “market traders” [N = 3], and “restaurant owners” [N = 4]) involved in the meat trade. Results indicate that hunting for trade is still a secondary livelihood activity, with more than half of our respondents selling hunted wildlife within their home villages. Hunters, intermediaries, market traders, and restaurant owners are involved in longer-distance wild meat trade and their roles are well defined from hunting to trading. The trade in wild meat along the coast of the BHP mirrors patterns found in other parts of the tropics. Market-oriented hunting that is emerging along the coast of the BHP may increase hunters' dependence on trading, which may increase the number of stakeholders involved. Consequently, wild meat harvest rates may be affected as modern techniques are used to increase the efficiency of hunting. Improving local agriculture productivity may be important to boost incomes and reduce the need to supplement income by selling wild meat. Development and conservation efforts should be focused on creating jobs for remote rural communities and to changing the behavior of wild meat purchasers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".