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Record W4410163098 · doi:10.18280/ijsdp.200427

Charting a Greener Future: Collaborative Governance Dynamics in Pekanbaru Sustainable Waste Management

2025· article· en· W4410163098 on OpenAlexvenueno aff
Trio Saputra, Sulaiman Zuhdi

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceCollaborative governanceBusinessEnvironmental planningEnvironmental economicsEnvironmental resource managementEnvironmental scienceEconomicsFinance

Abstract

fetched live from OpenAlex

The centralized policy of local government governance in waste management by applying strict laws and regulations has led to failure.This is evidenced by the increasing trend in landfill waste from year to year.There is a need for improvements and changes to local government policies in governance through the involvement of the private sector and the community.The purpose of this study is to explore the changes in the early stages of two collaboration initiatives: one relying on the traditional collaboration model centered on local government, and the other using a collaboration model involving the private sector and the community in the city of Pekanbaru.This study employs qualitative methods, including 15 indepth interviews with local government officials, private sector representatives, and community leaders, along with an analysis of policy documents and waste management reports from Pekanbaru City.This multi-source approach aims to explore the early-stage shifts from traditional, centralized governance to collaborative waste management involving multiple stakeholders.Key findings suggest that local governments have shifted their role to facilitators, encouraging collaboration with the private sector and community through platforms such as waste banks.The study proposes a new model for collaborative governance in sustainable waste management.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.004
GPT teacher head0.233
Teacher spread0.229 · 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 designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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