Public Participation and the Development of Transportation Infrastructure towards Sustainable Transportation and Regional Development in Medan, North Sumatra, Indonesia
Bibliographic record
Abstract
The purpose of this study is to analyze and explain the influence of public participation and the development of transportation infrastructure towards sustainable transportation and regional development in the city of Medan in North Sumatra Indonesia. This research was conducted by an explanatory approach using primary data with purposive sampling technique method based on criteria of 300 respondents. Data analysis techniques used was SEM (Structural Equation Modeling). The results of this study indicate that public participation has positive influence on sustainable transportation in the city of Medan, the development of transportation infrastructure has positive effect on sustainable transportation at the city of Medan, public participation has positive effect on the regional development in the city of Medan, the development of transportation infrastructure has positive effect on the regional development in the city of Medan, sustainable transportation has positive effect on the regional development in the city of Medan, public participation has indirect influence to the regional development through sustainable transportation in the city of Medan, and the development of transportation infrastructure has indirect influence to the regional development through sustainable transportation in the city of Medan, North Sumatra Indonesia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".