Pengaruh Tingkat Komitmen Lingkungan Freight Forwarder Terhadap Respon Kebijakan Green Freight Transport
Bibliographic record
Abstract
Kegiatan transportasi barang memberikan dampak negatif berupa polusi udara yang cukup signifikanpada lingkungan. Kebijakan uji kir kendaraan bermotor saat ini masih belum efektif untuk mengurangiemisi polusi udara karena emisi co2 harus dikontrol melalui pengurangan konsumsi bahan bakar.Diperlukan kebijakan yang dapat mengurangi polusi udara melalui pengurangan konsumsi bahan bakarkendaraan bermotor dengan pendekatan ASI (Avoid, Shift, Improve). Respon freight forwarder sebagai pihakyang terkena dampak langsung kebijakan akan berbeda-beda, diperkirakan dipengaruhi oleh tingkatkomitmen lingkungan perusahaan. Analisis klaster digunakan untuk mengelompokkan tingkat komitmenlingkungan freight forwarder, sedangkan untuk mengetahui pengaruh tingkat komitmen lingkungan freightforwarder terhadap respon kebijakan green freight transport dilakukan analisis Anova satu jalur. Hasil analisisklaster menunjukkan hampir separuh perusahaan (40% atau 24 perusahaan) mempunyai tingkat komitmenreaktif, 33 % (20 perusahaan) perusahaan mempunyai tingkat komitmen akomodatif dan 27% (16 perusahaan)perusahaan mempunyai tingkat komitmen proaktif. Dari uji hipotesis, dapat diketahui bahwa perusahaandengan tingkat komitmen yang berbeda (proaktif, akomodatif, reaktif) tidak memberikan respon yangberbeda terhadap usulan kebijakan green freight transport.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".