Assessing Community-based Natural Resources Management at Lake Naivasha, Kenya
Bibliographic record
Abstract
Lake Naivasha became a focus of international attention when it attained a Ramsar status in 1995. The lake is the major source of freshwater for the Kenya’s booming horticultural industry. There is growing evidence of excessive water abstraction to meet both the economic and social needs of communities living around the lake. Recently its management and conservation has been devolved to the local communities in a bottom-up participatory approach to replace the traditional top-down management strategy. This paper assesses the role of Community-Based Natural Resources Management (CBNRM) in achieving sustainable water management at Lake Naivasha. The results showed a diversity of stakeholders with different interests competing amongst themselves for water access which is affecting both the lake hydrology and the surrounding wetland ecosystems. Due to human activities, the effects of climate change, and the lack the capacity of various stakeholders to finance and follow through with conservation projects, the lake water has been drying over the years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".