Analisis Pergeseran Struktur Ekonomi dan Penentuan Sektor Unggulan Provinsi Kalimantan Selatan Tahun 2018-2022
Bibliographic record
Abstract
Struktur perekonomian di Provinsi Kalimantan Selatan didominasi sektor pertambangan dan penggalian. Untuk mengurangi ketergantungan pada sektor pertambangan dan penggalian, perlu dilakukan transformasi struktur ekonomi ke sektor lain yang berkelanjutan. Penelitian ini bertujuan untuk mengetahui perubahan struktur ekonomi dan alternatif sektor unggulan di Provinsi Kalimantan Selatan. Penelitian ini merupakan penelitian deskriftif kuantitatif yang dilaksanakan di Provinsi Kalimantan Selatan. Analisis yang digunakan yaitu Location Quotient, Dynamic Location Quotient, Shift Share dan Tipologi Klassen. Data yang digunakan merupakan data sekunder PDRB menurut lapangan kerja Atas Dasar Harga Konstan dari 2018 hingga 2022. Hasil penelitian menunjukkan bahwa sektor yang memenuhi kriteria unggulan yaitu sektor pertambangan dan penggalian; sektor pengadaan air, pengelolaan sampah, limbah, dan daur ulang; sektor transportasi dan pergudangan; dan sektor jasa pendidikan. Pengembangan tiga sektor unggulan selain sektor pertambangan dan penggalian, diharapkan mampu meningkatkan pertumbuhan perekonomian di Provinsi Kalimantan Selatan secara berkelanjutan.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".