Peningkatan Implementasi Tri Hita Karana Pada Keberlanjutan Pengelolaan Penyediaan Air Minum Perdesaan di Kabupaten Buleleng
Bibliographic record
Abstract
Rural drinking water management in Buleleng Regency is served by rural drinking water supply (PAM Des) and community-based drinking water supply (pamsimas). The local wisdom of Tri Hita Karana (THK) as a sustainability concept is also applied in the management of drinking water supply systems through a participatory approach.The method used to analyze the sustainability index for PAM Des management uses the Multi Dimensional Scaling (MDS) method. Multi Dimensional Scaling also analyzes lever factors that are sensitive to sustainability.The results of the analysis using Rap analysis show that the sustainability index value for the ecological dimension is 70.11%, the economic dimension is 46.00%, the socio-cultural dimension is 50.65%, the technological dimension is 46.89% and the institutional dimension is 49.74%. The sustainability index value for the ecological and socio-cultural dimensions with a value above 50% is quite sustainable. The sustainability index value of the economic, technological and institutional dimensions of rural drinking water supply management is less sustainable.The leverage factors obtained from the analysis of 5 (five) dimensions of sustainability are 19 factors. Leveraging factor attributes in the ecological and socio-cultural dimensions are maintained and improved through the Tri Hita Karana implementation strategy. Improving the Tri Hita Karana implementation strategy in the economic, technological and institutional dimensions must be carried out by improving, upgrading the PAM Des infrastructure and also regulations that strengthen the management of PAM Des.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".