MétaCan
Menu
Back to cohort
Record W4402217866 · doi:10.36985/xhxkk316

Peranan Alokasi Dana Desa Terhadap Kesejahteraan Masyarakat Nagori Nagur Usang Kecamatan Tapian Dolok Kabupaten Simalungun

2020· article· id· W4402217866 on OpenAlexaff
Benny S H Saragih, Robert Tua Siregar, Marihot Manullang, Sofiyan Matondang

Bibliographic record

VenueJurnal Regional Planning · 2020
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsTraditional medicineMedicine

Abstract

fetched live from OpenAlex

Tujuan penelitian ini adalah menganalisis alokasi dana desa terhadap kesejahteraan masyarakat nagori nagur usang kecamatan tapian dolok Kabupaten Simalungun. Keberhasilan program alokasi dana desa membutuhkan dukungan semua pemangku stake holder nagori nagur usang. Populasi penelitian ini adalah penduduk nagori nagur using yang terdaftar di sensus penduduk yang berjumlah 2384 jiwa. Dengan mengggunakan rumus penarikan sampel, maka sampel penelitian sebesar 100 orang. Penelitian ini menggunakan regresi sederhana, metode analisis dan pengujian hipotesis. Penelitian ini memberikan informasi bahwa alokasi dana desa memiliki pengaruh terhadap tingkat kesejahteraan masyarakat. Pengolahan data dilakukan dengan menggumpulkan data hasil kuesioner dan pengolahannya menggunakan SPPS. Dari hasil penelitian diketahui bahwa pengaruh alokasi dana desa terhadap kesejahteraan masyarakat pemilih sebesar 0,277 atau 27,7 %. Jadi dapat disimpulkan bahwa alokasi dana desa mempunyai pengaruh yang signifikan terhadap kesejahteran masyarakat di nagori nagur using kecamatan tapian dolok Kabupaten Simalungun. Penelitian ini memberikan sumbangan pemikiran dan saran kepada Pemerintah nagori nagur usang bahwa alokasi dana desa membutuhkan dukungan penuh dari Pemerintah Kabupaten simalungun, masyarakat berpartisipasi aktif dalam pembangunan desa dan meningkatkan kesejahteraan masyarakat desa

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.089
GPT teacher head0.249
Teacher spread0.160 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJurnal Regional PlanningSame topicEconomic Growth and Fiscal PoliciesFrench-language works237,207