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Record W4298314972

Competitiveness of Port-Cities: The Case of Marseille-Fos - France

2012· preprint· en· W4298314972 on OpenAlexaff
Lucie Billaud, Jasper Cooper, Claude Comtois, Suzanne Chatelier, Léonie Claeyman, César Ducruet, Caroline Guillet, Charlotte Lafitte, Jing Li, W.J.J. Manshanden, Evgueny Poliakov, Nicolas Winicki

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2012
Typepreprint
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPort (circuit theory)BusinessEconomic geographyGeographyRegional scienceAgricultural economicsEconomicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

This working paper offers an evaluation of the performance of the port of Marseille-Fos, an analysis of the impact of the port on its territory and an assessment of policies and governance in this field. It examines declining port performance over the last decades and identifies the principal factors that have contributed to it. The effect of the ports on economic and environmental questions is studied and quantified where possible. The value added of the port cluster of Marseille-Fos is calculated and its interlinkages with other economic sectors and other regions in France delineated. The paper outlines the impact of the ports? operations, and shows how their activities spill over into other regions than the one in which the port of Marseille-Fos is located. The major policies governing the ports are assessed, along with policies governing transport and economic development, the environment and spatial planning. These include measures instituted by the port authorities, as well as by local, regional and national governments. Governance mechanisms at these different levels are described and analysed. Based on the report?s findings, recommendations are proposed with a view to improving port performance and increasing the positive effects of the port of Marseille-Fos on its territory.

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)0.002
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: none
Teacher disagreement score0.719
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.001
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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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 designTheoretical or conceptual
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

Citations0
Published2012
Admission routes1
Has abstractyes

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