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Record W2897120545

Greening Arctic Cruise Shipping Through Law and Technology: A Role for China

2018· article· en· W2897120545 on OpenAlexaffabout
Stefan Kirchner

Bibliographic record

VenueLaCRIS (University of Lapland) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsCruiseArcticChinaContext (archaeology)The arcticBoomPollutionAir pollutionEnvironmental planningNatural resource economicsBusinessEnvironmental scienceEnvironmental protectionGeographyPolitical scienceOceanographyEnvironmental engineeringLawEconomicsEcology
DOInot available

Abstract

fetched live from OpenAlex

Increased shipping in the Arctic will mean not only increasing tourism revenue for local communities but, more importantly in the long run, increasing health risks for local residents. The overwhelming majority of ships is powered with fossil fuels and concerns over emissions have led to the creation of Emission Control Areas, such as the Sulphur Emissions Control Area (SECA) in the Baltic Sea, the North Sea and along much, but not all, of the coasts of the United States and Canada. None of the existing SECAs includes areas north of the Arctic Circle. This means that coastal communities, in particular in cruise ship destinations, are put at risk from high emissions of SO2. The research presented here shows that China has the potential to play several roles in contributing to the protection of coastal communities in the Arctic and in safeguarding the human right to live in a healthy environment, which has long been recognized by the European Court of Human Rights. It will be shown that China has the potential to use international forms of cooperation in the context of the work of the International Maritime Organization in order to support the establishment of a SECA for the entire Arctic Ocean but can also profit from it in the long run, provided that China’s shipbuilding industry becomes able to meet the needs of more environment conscious ship buyers.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score1.000

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 studies0.0010.001
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 head0.020
GPT teacher head0.270
Teacher spread0.249 · 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.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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