Bibliographic record
Abstract
Masa depan inovasi daerah merupakan isu krusial yang mulai muncul sejak lima tahun awal pelaksanaan otonomi daerah. Inovasi merupakan salah satu pilar penting untuk meningkatkan daya saing kinerja pemerintahan. Tujuan utama dari penelitian ini adalah mengetahui keberlanjutan inovasi daerah Kota Malang. Guna mencapai tujuan tersebut, penelitian ini menggunakan metode analisis observasional (observational analysis). Hasil studi ini menunjukan dari ke-13 inovasi yang digagas oleh 12 Perangkat Daerah (PD), sebanyak 9 inovasi yang mengalami perkembangan (blooming), dan 4 inovasi stagnan (tidak berkembang). Studi ini mengidentifikasi penghambat berkembangnya inovasi daerah Kota Malang diantaranya; Pertama, rendahnya tingkat ketersediaan alokasi anggaran yang memadai dan pengelolaan dan pertanggungjawaban anggaran yang masih terbatas. Kedua, konsistensi dalam mengembangkan SDM pengelola inovasi masih rendah. Ketiga, minimnya pemanfaatan data, informasi, dan pengetahuan terkait kinerja inovasi. Keempat, tidak tersedianya unit khusus yang didedikasikan untuk terus mengembangkan inovasi. Kelima, aspek legal formal yang menjamin keberlangsungan inovasi belum tersedia.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.021 |
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".