Bibliographic record
Abstract
Kota Malang mendapatkan gelar sebagai kota terbesar kedua di Jawa Timur setelah Surabaya, Kota Malang yang memiliki beragam julukan dikarenakan potensi wilayah, keadaan alam yang indah dan iklimnya yang sejuk. Indeks Daya Saing Daerah (IDSD) adalah ukuran yang menggambarkan kondisi dan kemampuan suatu daerah dalam mengoptimalkan pemanfaatan seluruh potensi yang dimilikinya guna tercapainya kesejahteraan yang tinggi dan berkelanjutan. Daerah dengan skor IDSD tertinggi diartikan sebagai daerah yang berhasil secara optimal memanfaatkan segala potensi yang dimiliki sebagai upaya menciptakan daya saing dan kesejateraan yang tinggi dan berkelanjutan. Penelitian ini menjelaskan indeks daya saing daerah Kota Malang tahun 2018 beserta komponen pembentuknya.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.018 | 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".