Smart and Sustainable Port Performance in Thailand: A Conceptual Model
Bibliographic record
Abstract
Global seaports are interested in the concept of smart and sustainable ports that many have an impact on global trade and economics. This research aims to find the main factors and indicators of smart and sustainable port management, and confirm the factors of smart and sustainable port management using a case study of the Eastern Economic Corridor (EEC) in Thailand. Mix method approach, qualitative research is used in the data collection with a total of three databases in 2015-2021. Content analysis with triangulation data is utilized to analyze these data finding the factors and indicators of smart and sustainable port management. Quantitative research is used confirmatory factor analysis (CFA) for confirming the factors and indicators and developing a conceptual model of smart and sustainable port performance. As the result, a conceptual model with three main factors is shown including the smart port environment, the smart port society, and the smart port economy. These main factors and indicators are represented as three factors and seventeen indicators of confirming explanation. This conceptual model for the introduction of port development explains smart and sustainable port performance and the key indicators to achieve port practice for improving international standards of smart and sustainable ports.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".