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Record W2955659197 · doi:10.5539/ibr.v12n7p141

Strategy to Build a Transshipment Port as a Catalyst to Achieving Critical Mass for Sabah’s Economic Growth

2019· article· en· W2955659197 on OpenAlexvenueno aff
Ngui Min Fui Tom

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsTransshipment (information security)Port (circuit theory)BottleneckBusinessTrade facilitationService (business)Critical mass (sociodynamics)PaceGovernment (linguistics)ChinaIndustrial organizationOperations managementEconomicsMarketingOperations researchInternational tradeEngineeringTrade barrier

Abstract

fetched live from OpenAlex

Digital transformation has led to a new era of port development at an unprecedented pace. China represents a large percentage of total global trades, navigating the maritime silk-road to various global and regional ports. In Malaysia, the lack of concrete justifications for the issue of transhipment port strategy leads to a debatable framework. Hence, the aim of the paper is to critically discuss the strategy to build a transhipment port as a catalyst to achieving critical mass for economic growth in Sabah. The study draws heavily on existing literature on the theoretical evidence and the possible factors that shape strategy to build transshipment port in Sabah. Based on reviewed literature, various resultant strategies adopted to stand for their interest are discussed. In this way, this paper provides not only theoretical insights, but also strategically guides managers of organisations in Sabah, government, and businesses values towards building a transshipment port in Sabah to effectively retire cabotage policy to reduce cost, enhance port throughput, develop hinterland for critical mass, enhance ports-economic clusters connectivity, eliminate capacity bottleneck, unlock natural resources export potential, align port service towards regional port users’ needs and to give regional port powers a run for their money.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score1.000

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)0.0010.001

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.

Opus teacher head0.047
GPT teacher head0.364
Teacher spread0.317 · 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.

Study designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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