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Record W2581282890 · doi:10.29313/mimbar.v32i2.1853

Relationship Between Solid Waste Service Characteristics and Income Level in Metropolitan Bandung Raya

2016· article· en· W2581282890 on OpenAlexaff
Sri Maryati, An Nisaa’ Siti Humaira, Husna Tiara Putri

Bibliographic record

VenueMIMBAR Jurnal Sosial dan Pembangunan · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMetropolitan areaMunicipal solid wasteService (business)UrbanizationPaymentBusinessHousehold wasteWaste managementEconomic growthGeographyFinanceEngineeringEconomicsMarketing

Abstract

fetched live from OpenAlex

Rapid urbanization process has stimulated the emergence of the metropolitan area, including Metropolitan Bandung Raya. The development of the metropolitan region is not equipped by infrastructure. Generally, the level of service of infrastructure varies based on income level. The purpose of this research is to identify the relationship between solid waste service characteristics and household income level. Solid waste service characteristics are measured from waste handling and disposal, waste collection officers, the frequency of waste collection, and fees and payment. The results of the analysis show that there is a relationship between solid waste service and income level: the higher the income, the better the solid waste service. The followings are some significant findings found in this research: (a) solid waste service in housing developed by the developer is better compared to those in self-help housing, and (b) solid waste service in the urban area is better compared to those in peri-urban and rural area.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.668

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 studies0.0010.000
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 head0.069
GPT teacher head0.314
Teacher spread0.245 · 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 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

Citations3
Published2016
Admission routes1
Has abstractyes

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