Pengaruh PAD, DAU, DAK dan DOK terhadap Produk Domestik Regional Bruto
Bibliographic record
Abstract
A B S T R A C T PDRB is defined as a number of additional values that is resulted of whole efford units in a region or as whole numbers of the final goods and fees from the entire of economic units of a region. PDRB’s level is able to describe a regional economic growth. The hight of economic growth level is appointed with the hight of value’s level of PDRB, in which is that region has economic progress. PAD, DAU, DAK, and DOK are the factors which have contribution into PDRB construction. According to that the purpose of this research is to identify how many influences among PAD, DAU, DAK, and DOK to PDRB in the Papua province. In doing this research is used multiple regretion analization method by using 5 years data of PAD, DAU, DAK, and DOK during 2009 to 2013. There are significant research result PAD, DAU, DAK and DOK to PDRB. A B S T R A K PDRB didefinisikan sebagai jumlah nilai tambah yang dihasilkan dari seluruh unit efford di wilayah atau sebagai nomor seluruh barang akhir dan biaya dari seluruh unit-unit ekonomi suatu daerah. Tingkat PDRB adalah mampu menggambarkan pertumbuhan ekonomi regional. Hight tingkat pertumbuhan ekonomi ditunjuk dengan hight tingkat nilai tentang PDRB, di mana adalah bahwa daerah memiliki kemajuan ekonomi. PAD, DAU, DAK, dan DOK merupakan faktor yang memiliki kontribusi dalam pembangunan PDRB. Menurut bahwa tujuan dari penelitian ini adalah untuk mengidentifikasi berapa banyak pengaruh antara PAD, DAU, DAK, dan DOK terhadap PDRB di provinsi Papua. Dalam melakukan penelitian ini digunakan metode multiple regresi Alat analisa dengan menggunakan 5 tahun data PAD, DAU, DAK, dan DOK selama 2009 hingga 2013. Ada signifikan hasil penelitian PAD, DAU, DAK dan DOK terhadap PDRB. JEL Classification: H83, M12
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".