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Record W4405713745 · doi:10.25158/l13.2.15

Infrastructures of Transiency: On Cruise Ships

2024· article· en· W4405713745 on OpenAlexaffabout
Richard L. Simpson, Constance Dijkstra, Luc Renaud, Francesca Savoldi, Andrew Culp

Bibliographic record

VenueLateral · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCruiseAeronauticsCruise missileMarine engineeringComputer scienceEnvironmental scienceAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Cultural Studies Association’s Environment, Space & Place Working Group Co-Chair Richard Simpson discusses the local, global, and transnational impact of cruise ships and the cruise ship industry with Constance Dijkstra, International Maritime Organization (IMO) policy manager for the advocacy group T&E, Karla Hart, co-founder of the Global Cruise Activist Network, and Luc Renaud, Associate Professor at the Department of Urban and Tourism Studies at the University of Quebec in Montreal. This podcast is accompanied by a scholarly commentary by Francesca Savoldi.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0080.016
Scholarly communication0.0080.008
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.001

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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
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
Published2024
Admission routes2
Has abstractyes

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