Cage fish farming for the livelihood improvement of the local people in Kulekhani, Nepal
Bibliographic record
Abstract
In Nepal, cage fish culture in the lake and reservoir has plays important role in the improvement of the livelihood of local people through creating employment, improving incomes, and assuring food security. Cage fish farming in Kulekhani was implemented with the support of the International Development Research Centre, Canada (IDRC) and the Nepal government to improve the livelihood of those people who were displaced by the impoundment of the reservoir. The local people in Kulekhani reservoirs have been practicing semi-intensive fish farming over extensive farming system because of its satisfactory production with low investment cost. This study is conducted to find out how cage fish farming in Kulekhani contributes to the changes in the local people’s livelihood. The study also aimed at finding out the social and economic obstacles faced by potential local people for cage fish farming. In Kulekhani, Balami, Majhi, Tamang, and Pode are those ethnic communities who are involved mainly in fishing for their living, and recently they are adopting cage fish farming for better living. \nCage fish culture has helped the displaced people, poor, indigenous, and marginalized communities by providing income, food security, and a healthy community where everyone is living without being discriminated. Nowadays, cage fish culture in Kulekhani reservoir has been observed as valuable to all people from the community, not only to those who were displaced by the impoundment of the reservoir, landless, poor, or untouchable groups. Cage fish culture has gained popularity in Kulekhani but the establishment of more cages in the area is hampered due to various problems. The result shows that the major constraints related to the adoption of cage fish farming are lack of knowledge, lack of capital and discrimination between upper and lower caste, rich and poor people were major.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".