The Importance of Bushmeat in Household Income as a Function of Distance from Protected Areas in the Western Serengeti Ecosystem, Tanzania
Bibliographic record
Abstract
Bushmeat hunting is widespread in villages adjacent to protected areas in Western Serengeti. However, little information is available about the role of bushmeat income in the household economy as a function of distance from the protected area boundary, preventing the formulation of informed policy for regulating this illegal trade. This study was conducted in three villages in Western Serengeti at distances of 3 (closest), 27 (intermediate) and 58km (furthest) from the boundary of Serengeti National Park to assess the contribution of bushmeat to household income. The sample consists of 246 households of which 96 hunted or traded bushmeat, identified using snowball sampling through the aid of local informers. The average income earned from bushmeat was significantly higher for bushmeat traders than hunters. The contribution of bushmeat to household income was significantly higher in Robanda the village closest to the protected area boundary compared to Rwamkoma and Kowak, the more distant villages. A Heckman sample-selection model reveals that household participation in hunting and trading bushmeat was negatively associated with distance to the protected area boundary and with the household head being female. Household reliance on bushmeat income was negatively associated with age and gender of the household head and distance to the protected area boundary. Hence, efforts to reduce involvement in hunting, and trading bushmeat should target male-headed households close to the protected area boundary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".