Bibliographic record
Abstract
Penelitian ini bertujuan untuk: (1) menyusun dokumen Pengukuran Angka Ekonomi Kreatif yang berupa PDRB ekonomi kreatif dari 16 subsektor ekonomi kreatif di Kota Malang tahun 2018. (2) memproyeksikan atau melakukan forecasting angka PDRB ekonomi kreatif Kota Malang untuk tahun 2019 sampai 2023. Pendekatan yang digunakan adalah pendekatan kuantitatif dengan metode survei dan data sekunder. Survei dengan sampel sebesar n = 416 lapangan usaha. Perhitungan PDRB Ekonomi kreatif pada penelitian ini menggunakan pendekatan PDRB produksi, yaitu jumlah nilai tambah seluruh aktivitas ekonomi, dimana nilai tambah diperoleh dari output dikurangi konsumsi antara. Hasil penelitian ini adalah: (1) PDRB ADHB total ekonomi kreatif Kota malang melalui survei sebesar Rp. 7,70714 triliun dan PDRB ADHK total ekonomi kreatif Kota malang melalui survei sebesar Rp. 5,616 triliun. (2) Proyeksi dari pertumbuhan PDRB ekonomi kreatif Kota Malang berdasarkan harga konstan meningkat setiap tahunnya, yaitu tumbuh sebesar 8,10% pada tahun 2019, 8,20% tahun 2020, 8,44% tahun 2021, 8,70% tahun 2022 dan tumbuh sebesar 8,93% pada tahun 2023. (3) subsektor unggulan dari ekonomi kreatif Kota Malang adalah kuliner, kriya, dan Fashion, sedangkan untuk subsektor prioritas adalah Aplikasi dan Game Developer; Film, Animasi dan Video; Musik.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.006 | 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".