DETERMINAN PEREKONOMIAN DI KABUPATEN/KOTA PROVINSI BALI TAHUN 2018 – 2022
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis pengaruh tenaga kerja, kriminalitas, pendidikan, dan pengeluaran pemerintah terhadap pembangunan ekonomi di Provinsi Bali. Regresi data panel digunakan untuk penelitian ini, yang mencakup penggabungan data cross-sectional dari 9 kabupaten/kota di Provinsi Bali selama lima tahun (2018-2022) dengan menggunakan teknik Ordinary Least Square dengan data sekunder. Adapun data penelitian bersumber dari situs resmi BPS Provinsi Bali. Penelitian ini menghasilkan temuan terkait dengn nilai ambang batas signifikansi sebesar 5% (0,05), hal ini mengartikan bahwa tenaga kerja, pendidikan, dan investasi pemerintah memiliki pengaruh yang baik (positif) terhadap pertumbuhan ekonomi di Provinsi Bali dari tahun 2018 hingga 2022. Di sisi lain, indeks kriminalitas memiliki pengaruh negatif, namun tidak ada satupun dari variabel ini yang memiliki pengaruh parsial yang substansial terhadap pertumbuhan ekonomi Bali pada tahun 2018-2022
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.015 |
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; both teacher heads agree on what is shown here.
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".