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Record W3171927197 · doi:10.6000/1929-4409.2021.10.123

Regulatory Reconstruction of Waste Management to Achieve Efficient and Sustainable Environmental Management

2021· article· en· W3171927197 on OpenAlexvenueno aff
Moh. Sidik, Edy Lisdiyono

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Analysis in Indonesia
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessGovernment (linguistics)Work (physics)Sustainable managementSustainable developmentEnvironmental planningWaste managementEnvironmental resource managementSustainabilityEngineeringEconomicsEnvironmental sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

One of the fundamental problems related to environmental management that continues to receive attention is waste. Likewise, waste management is still a serious problem faced by Pemalang, Central Java. This study seeks to discuss the reconstruction of waste management regulations in Pemalang Regency, Central Java, Indonesia. The results showed that the prevailing regulation is suboptimal and inefficient due to the presence of two concurrent waste management sectors, i.e., public work and environmental department. Besides, the community's low participation at the village level also hinders waste management. Therefore, waste management regulation reconstruction is necessary to address the issues of waste management in Pemalang Regency. The results of the present study highlight the legal aspects and demonstrated the reconstruction of the waste management regulation in Pemalang Regency to obtain sustainable and efficient environmental management. The result of the study showed that waste management could be optimized by focusing on one sector, i.e., the environmental department. In addition, the subdistrict or village government should be given the authority to participate in managing waste, which will create a synergy between the government and the community.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.294
Teacher spread0.276 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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