Sectoral Linkages in Pangandaran Regency’s Regional Development
Bibliographic record
Abstract
Pangandaran District is a newly formed region in West Java Province. Effective and competitive regional development requires support from the potential leading sectors in a region. Prioritization is needed because of limited resources that cannot facilitate the development of all sectors simultaneously. This analysis aims to identify leading sectors, sector interconnections, and multiplier effects on the economy of Pangandaran Regency. The results are that there are 5 (five) sectors that are comparatively and competitively superior, namely: (1) Accommodation, Food and Drink Providers, (2) Agriculture, Forestry and Fisheries (3) Transportation and Warehousing, (4) Wholesale and retail trade , Repair and (5) Real Estate. The largest backward linkages are achieved by the "other services" sector. This sector becomes a lever for the development of its own sector. Meanwhile, the largest forward linkages are agriculture, forestry, and fisheries. The output from the agricultural sector is widely used as an input by other sectors. Agriculture, forestry and fisheries sectors are key sectors. This is because agriculture, forestry, and fisheries have the largest intersectoral linkages and dispersal index. The “Other services” sector is the largest output multiplier. The sector with the largest GRDP multiplier is agriculture, forestry and fisheries. Proper allocation of resources can be done by prioritizing the leading sectors to improve the regional development of the Pangandaran District.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".