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Record W2040020210 · doi:10.1108/09600030310460990

Comparison of Asian and European logistics systems

2003· article· en· W2040020210 on OpenAlexaff
James H. Bookbinder, Chris Soon Heng Tan

Bibliographic record

VenueInternational Journal of Physical Distribution & Logistics Management · 2003
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsContext (archaeology)BusinessRanking (information retrieval)ExcellenceTier 2 networkTier 1 networkCluster (spacecraft)Operations managementMarketingIndustrial organizationComputer scienceGeographyPolitical scienceEconomicsTelecommunications

Abstract

fetched live from OpenAlex

This research compares the logistics systems of Asia and Europe and categorises them into distinct levels of logistics excellence. First, the context in Asia and in Europe is summarized. Then, attributes of a world‐class logistics system are proposed. By applying cluster analysis to data from authoritative sources, we objectively segregate European and Asian logistics systems into three logistics tiers. There are several surprises, the main one being that the UK is classified Tier 2 (not as favourable as Tier 1). A prioritized set of attributes that the UK could improve on to qualify for the Tier 1 group is suggested. Sensitivity analyses are conducted to determine changes to the classifications. After finding that the top‐ranking logistics systems of Europe and Asia are from Denmark and Singapore, respectively, those two countries are studied in detail to draw logistics lessons applicable elsewhere.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations93
Published2003
Admission routes1
Has abstractyes

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