Innovation in environmental assessment of community‐based projects in sub‐Saharan Africa
Bibliographic record
Abstract
Although environmental assessment (EA) has been applied to large resource and infrastructure projects in developing countries, recent innovations in EA concepts, processes and tools have facilitated its application to small projects focused on basic human and livelihood needs. This paper describes innovations in the application of EA to community projects implemented by Canadian nongovernment organizations and their partners in sub‐Saharan Africa. Innovations include: (1) the dovetailing of environmental constructs in neopopulism with a conceptual shift in EA toward participatory, transactive planning; (2) assessment methodologies adapted from participatory rural appraisal; (3) development of grassroots EA capacity; and (4) linkage of community EA with project planning. Community EA is demonstrated for five cases involving agriculture and charcoal retail in Uganda, water supply and flood control in Niger and integrated rural development in Zambia. Future prospects and challenges include cultural adaptation, community empowerment through realignment of power relationships, mutual development of local and national EA capacity and increased training and resources for enhancing grassroots EA capacity.
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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 | 0.041 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 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".