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Record W3088769435 · doi:10.32497/akunbisnis.v1i2.1234

Perhitungan Alokasi Dana Bagi Hasil Pajak Rokok Pada Badan Pengelola Pendapatan Daerah Provinsi Jawa Tengah Triwulan Iv Tahun 2016

2018· article· en· W3088769435 on OpenAlexaboutno aff
Akhsanur Rifai, Resi Yudhaningsih

Bibliographic record

VenueJurnal Aktual Akuntansi Keuangan Bisnis Terapan (AKUNBISNIS) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)RevenuePopulationJavaBusinessTax revenueFinanceEconomicsGeographyMedicineEnvironmental healthPublic economicsComputer science

Abstract

fetched live from OpenAlex

The reseach aims to know determine the procedure for calculating the allocation of funds from cigarette tax revenues quarter IV to Regency / City of Central Java Province On Central Java Regional Income Management Board. Writing method used are the method of description and exposition. The calculation of the allocation of revenue sharing funds for tobacco tax quarter IV which is in use is the realization of 2016. The results of the calculating the allocation of funds from cigarette tax revenues quarter IV to Regency / City between of Central Java Province On Central Java Regional Income Management with regulation governer 67 in 2014 Show result different. Different because that the share of cigarette tax data the number of population used is the data of population in 2014, when it should be calculated with the data of the population in 2015.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.004

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 head0.028
GPT teacher head0.220
Teacher spread0.192 · 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
Published2018
Admission routes1
Has abstractyes

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