Analisis Foreign Direct Investment di 6 Negara Penghasil Nikel Periode 2017-2021
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis bagaimana pengaruh market size, tingkat upah, tenaga kerja, inflasi dan sumber daya alam (produksi nikel) terhadap arus masuk FDI dari 6 negara penghasil nikel (terdiri dari Australia, Brazil, Canada, Filipina, Indonesia, dan Russia). Pertama, penulis menggunakan analisis panel data dengan model pooled least square yang dikumpulkan dari tahun 2017 hingga 2021 untuk memperkirakan hasil estimasi dari negara-negara tersebut. Kedua, analisis panel data menggunakan common effect model untuk mendapatkan hasil regresi yang optimal untuk regresi. Hasil penelitian menunjukkan bahwa tingkat upah, tenaga kerja, dan sumber daya alam (produksi nikel) adalah tiga faktor utama yang berpengaruh signifikan dalam mempengaruhi arus masuk FDI. Sedangkan, variabel market size dan inflasi menghasilkan hasil yang tidak berpengaruh signifikan dalam arus masuk FDI. Kemudian, untuk meningkatkan dari kelima sektor tersebut, pemerintah dari 6 negara penghasil nikel harus meningkatkan kualitas dan jumlah tenaga kerja, mengontrol inflasi, dan menciptakan aturan atau kebijakan baru khususnya untuk meningkatkan GDP serta pengelolaan pada nikel yang sudah diproduksi.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".