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Record W1994843950 · doi:10.1080/03088830902861128

Cruising in the Mediterranean: structural aspects and evolutionary trends

2009· article· en· W1994843950 on OpenAlexaff
Stefano Soriani, Stefania Bertazzon, Francesco Di Cesare, Gloria Rech

Bibliographic record

VenueMaritime Policy & Management · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDynamismPort (circuit theory)Context (archaeology)GeopoliticsModernization theoryCruiseCompetition (biology)BusinessMediterranean climateEconomic geographyService (business)EconomyEconomicsGeographyMarketingEconomic growthPolitical scienceEngineeringEcology

Abstract

fetched live from OpenAlex

In recent years the Mediterranean has grown so markedly within the global cruise market that it now ranks second in the world. Demand growth rates are constantly positive. Supply is steadily growing; major world companies are deploying more vessels in the area; many passenger terminals and ports are undergoing infrastructural modernization. Overall, the entire Mediterranean cruise sector holds a far greater appeal than in the recent past. Vertical integration processes have played a major role in increasing the dynamism of the sector and in affecting the competition among ports. In this context, Barcelona and Civitavecchia have emerged as the top ranking ports. Continued growth in the Mediterranean market can be expected in the near future; the main challenges are continued terminal and service modernization, geopolitical stability, particularly in its central-eastern part, and effective marketing strategies integrating port activity with inland resources.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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

Citations61
Published2009
Admission routes1
Has abstractyes

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