Analisis Implikasi Pengoperasian Trans Jateng Terhadap Biaya Transportasi Bekerja Buruh Industri (Studi Kasus: Koridor I Kedungsepur)
Bibliographic record
Abstract
Urban sprawl memicu permasalahan semakin tingginya biaya transportasi akibat keterbatasan layanan angkutan umum sehingga menyababkan ketergantungan terhadap penggunaan kendaraan pribadi. Fenomena ini telah muncul di berbagai Negara Barat dan sebagian besar Asia, tetapi di Indonesia fenomena ini bekerja secara natural dan Pemerintah memberikan solusi melalui pembangunan koridor transportasi baru termasuk pemberian subsidi transportasi untuk meringankan biaya perjalanan. Sebagai kasus studi digunakan metropolitan Kedungsepur yang menunjukkan bahwa dinamika ini unik karena terjadi pada kota-kota berukuran sedang dan menjadi semakin kompleks seiring dengan urbanisasi regional pada hiterlandnya. Artikel ini bertujuan mendeskripsikan peran kebijakan pemerintah dalam pembangunan jaringan transportasi Trans-Jateng koridor I dalam mereduksi biaya transportasi para pekerja industri. Hasilnya, buruh industri di area terurban metropolitan Semarang rata-rata menempuh perjalanan sejauh 44-54 km setiap harinya. Intervensi pemerintah dalam pembangunan Trans Jateng koridor I telah melibatkan subsidi yang mampu mereduksi biaya perjalanan buruh industri dari Rp306.081,10-367.297,32 menjadi Rp142.796,03-171.355,24 setiap bulannya. Diskusi ini memberikan kontribusi peran kebijakan sektor transportasi yang mendorong naturalisasi biaya perjalanan dalam studi transportasi perkotaan.
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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 | 0.002 | 0.004 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".