Have the Government's Efforts to Build Border Areas Succeeded? Case Studies in Kalimantan Corridor - Indonesia
Bibliographic record
Abstract
The purpose of development in the border area of Kalimantan Corridor is not only physical, and infrastructure development, but the development must reduce poverty, regional disparities, economic disparities, increase income and improve people's welfare. This research was conducted in the border area of the Kalimantan Corridor, which has a direct border with Malaysia. This study had two objectives: the first was using Panel Data Regression, analyzing the program's effect on economic performance such as poverty, human development index, and community per capita income. It used secondary data in 2015-2019, covering education, health, agriculture, marine, road, and market programs. The result is that no program positively affects development performance except marine to poverty. The second is by using the Analytic Network Process (ANP) method, creating a sustainable development strategy towards the border area of Kalimantan Corridor. The respondents were BNPP bureaucrats and academics who understood development planning in the border area. The data used is sourced from Grand Design of Border Area Management 2014-2025, and the result is a sustainable development strategy in the border area of the Kalimantan Corridor. From in-depth interviews, the strategies in the border area to support economic performance are infrastructure, natural resources and human development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".