Analysis of Community Self-Assistance Level in Water Resources Conservation in the Upper Areas of Renggung Watershed Lombok Island
Bibliographic record
Abstract
This study aims to analyze the initiative and level of community self-assistance in water resources conservation, analyze the influence of family socio-economic characteristics on the level of self-assistance in water resources conservation, and formulate a self-assistance conservation model in the upstream area of the Renggung watershed. This research was carried out in the upstream area of the Renggung watershed. Data were collected observation, in-depth interviews, document review. Data were analyzed based on a Likert scale and multiple regression. The results of the study are as follows: (1) Initiatives and the level of community self-assistance in the conservation of water resources in the upstream area of the Renggung watershed are classified in the “Low” category; (2) The socio-economic characteristics of the family that have a significant effect on the level of self-assistance are age and income. Age has a positive effect, while income has a negative effect; and (3) Self-assistance conservation models that can be developed are: Development of Village-owned Fruit and Ornamental Plant Tourism Parks; Productive Economic Business Empowerment; and Development of Conservation Crop Compensation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".