Household Reliance on Environmental Income in the Western Serengeti Ecosystem, Tanzania
Bibliographic record
Abstract
Pressures on protected areas (PAs) in Tanzania are increasing through the extractive use by surrounding communities. Understanding how environmental reliance varies in relation to distance from PAs and in relation to household’s socio-economic characteristics is important for PAs management and decision of poverty alleviation strategies. This study therefore aimed to quantifying the reliance on cash environmental income as a share in total household income over a gradient of distance from PA boundaries in Western Serengeti and evaluates how it is influenced by socio-economic characteristics. Data was collected through a semi-structured questionnaire of 150 households, randomly selected in three villages. Results indicate that environmental cash-income varies from 21.3% to 45.2% of the total annual cash-income, representing on average 37.8% of the total annual cash-income of all households surveyed. Households closest to the boundary of Serengeti National Park (SNP) are relatively more reliant on environmental income than those located relatively far. Environmental cash-income reliance is associated with household socio-economic factors including distance from SNP boundary, household wealth rank and absolute income from off-farm activities. The main sources of environmental cash-income are fuel-wood, construction materials and wild foods. Reducing environmental reliance requires promotion of off-farm activities, improved wood fuel stoves electricity and alternative sources of fuels.
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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 | 0.001 | 0.001 |
| 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 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".