PENGEMBANGAN KLASTER UMKM JAWA TENGAH BERBASIS \nKEPEMIMPINAN
Bibliographic record
Abstract
Peneletian tentang kepemimpindaan di perusahaan terutama perusahaan kecil sudah \nbanyak dilakukan karena pemimpin memiliki peran besar pada keberhasilan UMKM mengingat \npara pemimpin tersebut biasanya terlibat dalam bisnis SME sejak dilahirkan. Pendekatan \nentrereneural leadership digunakan pada penelitian pemimpin SME karena pemimpin memiliki \n[1]. entrepreneurial leaders in sustainable community organisations, including private, ‘for‐profit’, \ncommunity, and social enterprise organisations, two in Canada and two in the United Kingdom. \nInterpretation of the cases identifies the importance of the leaders’ principles and ethical values; \ncommunity involvement; opportunity scanning; and social innovation. \nPenelitian dilakukan dalam beberapa tahap. Pertama pemilihan sampel penelitian, dalam \nhal ini akan dipilih pemimpin klaster yang memiliki prestasi dan bertahan cukup lama dalam \nmeminpin klaster. Tahap kedua dilakukan wawancara yang menilai para pemimpin klaster \ntersebut. Wawancara menggunakan panduan wawancara yang berdasarkan pada teori \nEntrepreneural ledership Wawancara untuk mendapatkan data tentang pemimpin terpilih \ndilakukan terhadap tiga pihak yaitu anggota klaster, pendamping kaster tingkat kabupaten dan \npendamping klaster tingkat propinsi. Data tersebut dianaliais untuk mendapatkan karakteristik \npemimpin klaster yang selama ini dianggap berhasil.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.109 | 0.028 |
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".