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Record W4399125205 · doi:10.18280/ijsdp.190502

Fiscal Decentralization and Community Participation in Education Services in Deli Serdang Regency, Indonesia

2024· article· en· W4399125205 on OpenAlexvenueno aff
Mohammad Ridwan Rangkuti, Marlon Sihombing, Heri Kusmanto, Hatta Ridho

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationEconomic growthPolitical scienceBusinessDevelopment economicsEconomics

Abstract

fetched live from OpenAlex

The research aims to determine the community's participation in education services in Deli Serdang Regency and fiscal decentralization.This research is important and beneficial in identifying patterns and characteristics of income and expenditure in education services, exploring various types and patterns of community participation in education, and establishing the link between income and expenditure in education services and community participation in education in Deli Serdang Regency.The research used the constructivist Paradigm and a qualitative approach.The researchers gathered data through documentation technique, indepth interviews and discussions with various informants from the Regency, Regional People's Representative Council (DPRD), Sub-Regencies, Schools (elementary and junior high), and Community Figures.The research also gathered data through literature review, document study, and secondary data.The findings show that there is a financial imbalance between the Central Government and the Local Government, and horizontally between provinces and regencies/cities in Indonesia.Secondly, Deli Serdang Regency's Local Own-Source Revenue (PAD) is relatively high compared to other regencies in Sumatera Utara.However, the PAD has not optimized.Thirdly, the Expenditure in Education Services in Deli Serdang Regency for 2017-2022 is relatively stable.In addition, education Services allocated more funds to Indirect Expenditure (Operational Expenditure) than Direct Expenditure (Capital Expenditure).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.270
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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