Analisis Sektor Unggulan dalam Meningkatkan Perekonomian dan Pembangunan Wilayah Provinsi Jambi
Bibliographic record
Abstract
Based on pattern classification Typologi Klassen of the growth sectors of the economy in Jambi province makes the agricultural sector and the sector of mining and excavation are on the I quadrant i.e. as a sector that developed and developing fast, water procurement sector, trash, waste treatment and recycling, and education services sectors are at a quadrant II sectors advanced but that is depressed. After dianalis the pattern of growth sectors of the economy, may be known to the classification of economic sectors in the province of Jambi, for a deeper analysis of the sector required base with LQ method to find the base of the sector can be prioritized into the flagship sector. In accordance with the results of the analysis of the economic base by the method of LQ for the level of Jambi province are known to exist in four major sectors constituting the base sector of the economy. The fourth sector is agriculture, a sector of mining and excavation of the procurement sector, garbage, water, sewage treatment and recycling, and educational services. So, from both Typologi and Klassen LQ analysis it can be concluded that the economic sector in Jambi province which should be developed and can be prioritized into a flagship sector is agriculture, a sector of mining and excavation, the sector procurement of waste, water, sewage treatment and recycling, and education services sectors. Keywords: (1) GDP Jambi province; Indonesia'S GDP and (2) the rate of growth of GDP and contribution to Indonesia and Jambi province; (3) Data on the economic potential of Jambi province
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".