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Record W4303491744 · doi:10.24043/isj.404

A policy tool for island transport cost inequality: Exploration of the application of the Transport Equivalent Threshold on Greek islands

2022· article· en· W4303491744 on OpenAlexvenueno aff
Thanasis Kizos, Sofia Zafirelli, Ιoannis Spilanis, Dimitris Kavroudakis

Bibliographic record

VenueIsland Studies Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaSubsidyTicketCompetition (biology)GeographyPassenger transportDistribution (mathematics)InequalityEconomic geographyBusinessEconomicsTransport engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Many islands face accessibility problems that burden both the cost and the time of sea transport. In Greece, with more than 100 inhabited islands, the Transport Equivalent Threshold (TET) was recently introduced to support passengers (subsidizing ticket costs) and businesses (subsidizing transportation costs). The purpose of this study is to explore: (a) the spatial distribution of beneficiaries of TET; (b) the quantities, value and features of the freight transported to and from the islands; (c) to map the geography of the beneficiaries in relation to island size and location. Results reveal the unequal economies of Greek islands, and inter-island competition as well as the high disparities policy tools for businesses have to operate within. Moreover, the importance of radial transport links with the metropolitan area of Athens and the dependence of all islands on imports is highlighted. They also indicate the relative importance of geography in the magnitude and frequency of transport for goods and passengers among the Ionian and Aegean Seas, but also within the different clusters of the Aegean. The TET approach is one of the possible approaches that can and have been used to face transport and travel issues that people and businesses on islands face.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.311

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)0.0000.000

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 head0.060
GPT teacher head0.299
Teacher spread0.239 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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