Making community based environmental impact assessment work: Case study of a Dairy Goat and Root Crop Project in Tanzania
Bibliographic record
Abstract
Community Based Environmental Assessment (CBEA) is an approach used to collate information in order to identify and analyze the environmental impacts of planned, on-going and completed community development activities. This study was conducted with four villages in the Kongwa and Mvomero Districts of Tanzania where dairy goats and root crops were introduced as project interventions. The study addressed three key research questions. (1) What are key environmental challenges in the project villages? (2) What are the likely environmental impacts from this project? (3) What could be the community-perceived mitigation and enhancement measures for such impacts? Participatory appraisal methods including focus group discussions, participatory village resource mapping, transect walks and pair wise ranking were used in the field research. Research revealed several environmental challenges and depicted a number of environmental impacts (both negative and positive) including shortage of pasture, deforestation, conflicts between farmers and pastoralists and loss of biodiversity. In contrast to conventional regulatory environmental assessments that are required for larger-scale industrial projects in many countries, this study offers experiences from a small-scale development project where environmental impacts are assessed. Such methods could be applied to other small-scale development initiatives where positive or negative environmental impacts can be enhanced or ameliorated.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".