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Record W7008008364

Arctic shipping and China : Governance structure and future developments

2014· dissertation· en· W7008008364 on OpenAlexaboutno aff

Bibliographic record

VenueSkemman · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersState Oceanic AdministrationPolar Research Institute of ChinaChina National Offshore Oil Corporation
KeywordsChinaCorporate governanceArcticThe arcticPerspective (graphical)
DOInot available

Abstract

fetched live from OpenAlex

The goal of this thesis is to study China's shipping ambitions in the Arctic and the pertinent governing instruments.Arctic shipping poses significant challenges for Arctic governance with increased access to its oceans for shipping companies.Arctic transit is driven by demanding world markets in the West and the rising economic powers of the East, looking for the most cost-efficient routes.Rapid ice melt leads to better access for vessels, but other obsticles await those interested in Arctic shipping as the shortest route might not be the optimum choise.The Arctic shipping routes; the Northwest Passage; the Northern Sea Route; and the Central Arctic Ocean Route, are all at different phases when it comes to access for ships and governance prowess.The main governing bodies of Arctic shipping; UNCLOS; the International Maritime Organization; and Russia's and Canada's coastal state governance, must strike a balance between environmental protection and a feasible route for shipping companies worldwide.This is especially relevant to China's advancing economy and its need to diversify current shipping lanes.China has heightened its interest in the Arctic and now looks for economic opportunities in the North.This thesis brings together three elements of Arctic shipping: its prospect and feasibility, and China's interest and Arctic governance, with speculations whether the Arctic Ocean is a feasible transit route for China.

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 score0.748
Threshold uncertainty score0.858

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 studies0.0010.000
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 head0.008
GPT teacher head0.279
Teacher spread0.271 · 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
Published2014
Admission routes1
Has abstractyes

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