IDENTIFIKASI PRIORITAS SEKTOR-SEKTOR POTENSIAL GUNA MERANCANG STRATEGI PENGEMBANGAN PEMBANGUNAN MELALUI ANALISIS SHIFT-SHARE DAN SWOT
Bibliographic record
Abstract
Dalam rangka merumuskan perencanaan pembangunan pemerintah daerah yang baik, maka dibutuhkan suatu strategi pengembangan terhadap sektor-sektor potensial daerah yang dapat berfungsi sebagai pedoman dan arah pelaksanaan pembangunan guna meningkatkan perekonomiannya. Pada beberapa kasus, suatu daerah kurang jeli dalam mengidentifikasi sektor-sektor potensial sehingga banyak dijumpai suatu pengembangan dan atau pembangunan yang dilakukan suatu daerah tidak tepat sasaran, sehingga perlu adanya suatu penelitian yang dapat mengidentifikasi prioritas pengembangan sektor-sektor potensial sehingga pembangunan daerah sesuai pada sasaran serta rancanganstrategi pengembangan terhadap sektor-sektor tersebut. Dalam mengidentifikasi sektor-sektor potensial digunakan analisis shift share untuk menghitung perubahan pertumbuhan (pergeseran) sektor-sektor potensial guna menghasilkan prioritas. Sedangkan untuk merancang strategipengembangan menggunakan teknik SWOT. Dari hasil analisis shift share terhadap daerah Gresik, beberapa sektor potensial yang layak dikembangkan adalah sektor industri pengolahan, sektor perdagangan, hotel dan restoran dan sektor pertanian.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".