Analisis Pengaruh PMDN Dan PMA Terhadap PDRB Jawa Timur
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pada utamanya PDRB adalah total angka bertambah yang diterima pada semua sektor usaha di zona eksklusif maupun total jumlah barang serta jasa paling terakhir yang diterima pada semua unit perekonomian di zona eksklusif. Tujuan adanya dari penelitian ini yaitu agar melihat dampak penanaman modal dalam dan luar negeri pada PDRB di Wilayah Jawa Timur. Metode uji running yang dipakai yaitu penelitian kuantitatif. Sumber data yang dipakai mrupakan data bersifat sekunder dari BPS Jawa Timur, dengan data time series pada periode 2011-2020. Hasil analisis regresi berganda diketahui bahwa tingkat penanaman modal dalam negeri serta luar negeri memiliki pengaruh signifikansi terhadap tingkat PDRB daerah Jawa Timur
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it