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Record W4409210556 · doi:10.51172/jbmb.v6i1.425

Implementasi Kebijakan Penyediaan Air Bersih di Kabupaten Badung

2025· article· en· W4409210556 on OpenAlexaff
I Wayan Adi Sudiatmika, Ayu Kade Trisnawati

Bibliographic record

VenueJurnal Bali Membangun Bali · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPolitical scienceEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

Purpose: The fulfillment of clean water needs is a complex problem and requires comprehensive analysis. This study aims to analyze the implementation of clean water supply policies in Badung Regency to identify the strengths, weaknesses, opportunities, and threats. Research methods: This research uses a qualitative methodology with a SWOT analysis approach through data collection carried out by interview and documentation study methods. Results and discussion: The Badung Regency Government together with Perumda Air Minum Tirta Mangutama have implemented several policies to improve clean water supply. However, there are still several obstacles, such as limited raw water source capacity, rapid population growth so that service coverage does not increase significantly. The SWOT analysis results show that to meet the clean water needs in Badung Regency, the Weaknesses-Opportunities strategy is used with strategic policies in the form of developing and increasing the availability of raw water through the support of the central government and the Bali Provincial Government so as to increase the coverage of clean water services through developing alternative financing schemes, such as cooperation with the private sector, financial institutions, and the community. Implication: Efforts to improve the performance of clean water services in Badung Regency require a comprehensive strategy to optimize strengths and opportunities, as well as minimize weaknesses and threats in the implementation of clean water supply policies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.019
GPT teacher head0.239
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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