Prioritizing regional development strategies of free trade zone (FTZ) using the analytical hierarchy process (AHP) approach: A case study of Batam, Bintan, and Karimun (BBK), Riau islands province, Indonesia
Bibliographic record
Abstract
In Indonesia, creating growth centres, such as the Free Trade Zone (FTZ), is a strategy for regional development to stimulate economic expansion. The location of the FTZ of the Batam, Bintan, Karimun (BBK) area has been chosen strategically near Singapore and Malaysia. As a recognized national strategic area, the BBK area demonstrates substantial potential to augment the advancement of the national economy. This research employs the Analytic Hierarchy Process (AHP) to examine programs to prioritize strategies for advancing regional development. AHP has been demonstrated to facilitate the structuring of problems, identifying common opinions among respondents, and formulating solutions. The findings indicate that land preparation and permits are priority initiatives (0.122), followed by institutional reinforcement (0,111) and simplified requirements (0,071). As an archipelago, the enhancement of basic Infrastructure is also essential (0.052), mainly to improve connectivity. The next priority should be to improve the quality of human resources (0,051), fostering a favourable environment for investment. The outcomes of this investigation are expected to enrich the corpus of regional studies literature and provide valuable insights in assessing the efficacy of FTZ policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".