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Record W4224930741 · doi:10.21463/shima.151

Venice Without Cruise Ships: Hard Facts or Fake News?

2022· article· en· W4224930741 on OpenAlexaboutno aff
Alexander López

Bibliographic record

VenueShima · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCruiseTourismDecreePolitical scienceTransit (satellite)GeographyEconomyLawOceanographyEconomicsPublic transport

Abstract

fetched live from OpenAlex

For over a decade, social movements campaigning for the safeguarding of Venice and its lagoon have pointed out the many risks and negative impacts of cruise tourism, which include the potential collision with the historic city, water contamination, air pollution, underwater noise, erosion and the ‘touristification’ of the city space and local identity. Although several solutions have been proposed over the years, ranging from infrastructure projects to legal proceedings, in practice, ‘big cruises’ transited across Venice uninterruptedly. While, for some, cruise tourism meant economic growth and job creation, for others, the ‘big cruises’ were symbols of excessive consumption and environmental destruction. After the Covid-19 pandemic forced the industry to an unexpected impasse, resistance against cruises has gone global, and strong social movements have emerged in Mexico, United States, Canada, The Bahamas and Spain. In Italy, a new decree has banned the transit of ‘big cruises’ across the San Marco and Giudecca canals since August 1st, 2021. This essay reviews the events that led to this ban and examines the challenges that Venice still faces in relation to both mass tourism and cruise tourism.

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)0.002
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.006
Scholarly communication0.0090.011
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.003

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 head0.073
GPT teacher head0.331
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2022
Admission routes1
Has abstractyes

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