MONITORING DAN EVALUASI SISTEM INOVASI DAERAH (SIDA) KOTA MALANG 2020- 2023
Bibliographic record
Abstract
Kota-kota yang ada di Indonesia memiliki beragam kebutuhan dan tujuan yang akan dicapai oleh setiap pemimpinnya. Inovasi menurut Schumpeter (1934) memiliki arti, usaha mengkreasikan dan mengimplementasikan sesuatu menjadi satu kombinasi. Suatu kota dituntut untuk melakukan inovasi demi memenuhi kebutuhan dengan sumber daya yang terbatas. Sehingga diharapkan mampu menciptakan program unggulan untuk memenuhi hal tersebut. Salah satu hal yang menjadi hak dari daerah adalah pelaksanaan Penguatan SIDa (Sistem Inovasi Daerah) sesuai ketetapan yang diberikan sesuai Peraturan Bersama Menteri Riset dan Teknologi dengan Menteri Dalam Negeri Nomor 3 dan 36 tahun 2012. Urgensi terkait Penguatan SIDa bagi Kota Malang adalah untuk mengedepankan keunggulan dari potensi yang ada di Kota Malang. Selain itu, masuknya wabah Corona Virus Disease (Covid-19) yang mulai mewabah di Indonesia mulai bulan Maret lalu menjadikan perubahan terhadap inovasi cukup berdampak. Perubahan terkait anggaran menjadi aspek utama kegiatan monitoring dan evaluasi ini.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".