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Record W4405901106 · doi:10.31955/mea.v8i3.4956

PENGETAHUAN PERPAJAKAN, KESADARAN WAJIB PAJAK, AKUNTABILITAS PELAYANAN PUBLIK DAN SANKSI PERPAJAKAN TERHADAP KEPATUHAN WAJIB PAJAK KENDARAAN BERMOTOR

2024· article· id· W4405901106 on OpenAlexaff
Nabila Zahrotun Nikmah, Umaimah Umaimah

Bibliographic record

VenueJurnal Ilmiah Manajemen Ekonomi & Akuntansi (MEA) · 2024
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk mengidentifikasi hubungan antara kepatuhan wajib pajak kendaraan bermotor dengan faktor-faktor seperti pengetahuan perpajakan, kesadaran wajib pajak, akuntabilitas pelayanan publik, dan penerapan sanksi perpajakan. Kepatuhan wajib pajak kendaraan bermotor merupakan salah satu faktor utama dalam penerimaan pajak daerah di Indonesia, yang sangat dipengaruhi oleh tingkat kesadaran dan pengetahuan perpajakan. Selain itu, akuntabilitas dalam pelayanan publik dan penerapan sanksi perpajakan yang efektif diharapkan dapat meningkatkan tingkat kepatuhan tersebut. Penelitian ini menggunakan pendekatan kuantitatif, dengan mengumpulkan data melalui survei yang dilakukan pada sampel wajib pajak kendaraan bermotor di suatu wilayah. Diharapkan hasil penelitian ini dapat memberikan pemahaman tentang bagaimana keempat faktor tersebut dapat meningkatkan kepatuhan pajak kendaraan bermotor serta menjadi acuan bagi pemerintah daerah dalam merancang peraturan perpajakan yang adil.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.003

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 head0.032
GPT teacher head0.247
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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