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Record W4229008544 · doi:10.5539/jsd.v15n4p1

Smart and Sustainable Port Performance in Thailand: A Conceptual Model

2022· article· en· W4229008544 on OpenAlexvenueno aff
Weeraphong Sankla, Thanyaphat Muangpan

Bibliographic record

VenueJournal of Sustainable Development · 2022
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)Conceptual modelSustainable developmentBusinessConceptual frameworkEnvironmental economicsThe Conceptual FrameworkSustainable managementData collectionComputer scienceSustainabilityEnvironmental resource managementProcess managementEconomicsEngineeringPolitical scienceDatabase

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.193
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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