DAMPAK PEMBANGUNAN JALAN TOL TRANS JAWA TERHADAP PELUANG KEWIRAUSAHAAN
Bibliographic record
Abstract
The National Medium-Term Development Plan (RPJMN) for 2015-2019 and RPJMN for 2020-2024 emphasizes infrastructure development as the key to accelerating economic growth, especially through the implementation of a 2,500 km new toll road construction program. This program includes monumental projects such as the Trans Java Merak-Banyuwangi and Cikampek-Palimanan (Cipali) toll roads, which are the longest toll roads in Java. Evaluation of toll road development, in accordance with Government Regulation (PP) No. 39/2006, is a crucial step in assessing the success of public policy. However, PP No. 15/2005 emphasizes that the purpose of highway projects is to support economic growth and equitable development. In-depth research focused on the impact of the Trans Java toll road on economic growth in East Java. The results show the complexity and variation in the impact, with some toll roads increasing economic growth, while others decrease it. While theory suggests that infrastructure can accelerate economic growth, toll road development in East Java does not have an overall positive impact. Factors such as poor accessibility, high toll fees, and a lack of supporting infrastructure may be to blame. This impact is reflected in the decline in East Java's economic growth from 9.87% to 4.34% two years after toll road construction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
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".