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Record W160939466

New pattern of air market according to liberalization air transport between Korea and China

2007· article· en· W160939466 on OpenAlexaboutno aff
Woo-Choon Moon, Sangwook Lee, Younchul Choi

Bibliographic record

VenueJournal of the Korean Society for Aviation and Aeronautics · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsOpen skiesLiberalizationProsperityChinaInternational tradeAir transportOrder (exchange)BusinessLow-cost carrierInternational economicsEconomicsMarket economyEconomic growthGeographyFinance
DOInot available

Abstract

fetched live from OpenAlex

Air Transportation industry becomes more competitive that the restriction on new access to market were eased and relaxed. Liberalization of international air transport will continue, via bilateral and multilateral process. Korea, Japan, and China have expanded enormously the economic trade and cultural exchange bilaterally in the Northeast Asia, they are acknowledging the importance and necessity of improved connection, it order to face effectively other regional blocks of US-Canada, NAFTA, ASEAN, CLMV. In particular, nobody denies that it is urgent to liberalize bilaterally the air transport in Northeast Asia for promoting reciprocal benefits and prosperity. Recently while open skies bilateral agreements was signed between Korea-China in June, 2006. The agreements processes are too heavily influenced by flag carriers; leading to capacity/market sharing between the bilateral carriers in most markets, against the interest of consumers and overall economic interest of the nation. For successful operation of Northeast Air Market, it is need to set up development strategy paradigm by creating cross-border sub-regional (Northeast Asian) open skies bloc as well as preparing and creating of LCCs operations.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.240
Teacher spread0.219 · 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
Published2007
Admission routes1
Has abstractyes

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