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Record W2355390561

The Impact of Standard on the Competitiveness of Transportation Service Trade: An Empirical Analysis Based on Chinese Whole and Sectoral Panel Data

2014· article· en· W2355390561 on OpenAlexvenueno aff
Zhang Bao-yo

Bibliographic record

VenueInternational Business Research · 2014
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsTransportation industryService (business)CointegrationBusinessPanel dataInternational tradeTrade barrierTertiary sector of the economyIndustrial organizationEconomicsTransport engineeringMarketingEconometricsEngineering
DOInot available

Abstract

fetched live from OpenAlex

This article applies cointegration and Granger methods to demonstrate the relation between Chinese transportation industry standard and transportation service trade competitiveness, constructs multiple regression model, and analyzes the relation among standards of different transportation departments(shipping, air cargo and other transportation) and their competitiveness. This article gets the following conclusions: Chinese transportation standards have actively simulative effect on its trade competitiveness; standards of different transportation departments have different effect on transportation service trade, and sea transportation sectoral standard has the most effect on transportation service trade; the effect of transportation standards on the service trade competitiveness of different transportation sectors is different. The effect of transportation standard on the trade competitiveness of sea transportation department is most obvious; the research, which takes FDI as the substitute index of Chinese transportation market opening extent, finds that the increasing of transportation market opening can not improve Chinese transportation service competitiveness and has adverse influence on it.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.107
GPT teacher head0.404
Teacher spread0.296 · 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 designSimulation or modeling
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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