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Record W2035083417 · doi:10.1007/s10708-007-9092-x

What conditions supply chain strategies of ports? The case of Dubai

2007· article· en· W2035083417 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeoJournal · 2007
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsSimon Fraser University
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekAmerican University of Sharjah
KeywordsPort (circuit theory)Competition (biology)Supply chainBusinessIndustrial organizationCompetitive advantageEmpirical researchOperator (biology)Supply chain managementMarketingEngineering

Abstract

fetched live from OpenAlex

Recent academic debates about port competition have centered on the strategic responses of port authorities, operators, managers and owners to the emergence of global supply chains. The competitive performance of a port authority or operator, given the rise of the integrated logistics sector, depends increasingly on its strategic relationship to these supply chains and rather less on traditional port competition factors such as hinterland size and physical infrastructure. However, there are few empirical studies investigating the degree to which particular port actors are capable of inserting themselves into global supply chains. In this article we ask what factors condition the supply chain strategies of port actors. We hypothesize that the strategic supply chain choices of a port authority or operator are conditioned by the territorialized institutional framework in which the dominant actors in a port operate. We apply these insights through a case study of the transformation of Dubai Port Authority, and the rise of Dubai Ports World (DPW).

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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