PENGEMBANGAN SEKTOR UTAMA REGIONAL PENDEKATAN EFISIENSI TEKNIKAL DAN SIKLUS BISNIS Studi Kasus di Propinsi Bali
Bibliographic record
Abstract
The appropriate resource allocation on potential economic sectors can spur a faster economic growth. Knowledge on economic sectors which are efficient and have experiencing positive business cycle could ease the allocation of available economic resources. This research provides information on the technical efficiency of economic sectors as well as the business cycle of the economy in Bali. A stochastic frontier method is used to analyze the technical efficiency of economic sectors while the Bry-Boschan algorithm is used to estimate the business cycle of the economic sectors which are relatively efficient. The estimation result indicates that the efficient economic sectors in Bali are trade, hotel & restaurant (thr) sector and agriculture sector. Both sectors contribute most on the Bali’s economic output. At the end of the observation period, the economic business cycle in Bali is in a period of contraction. It is estimated that the next quarter will be the expansion period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".